{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cPkl_Mvu9fF5"
   },
   "source": [
    "# Crypto Trading Feature Importance Analysis for Deep Reinforcement Learning\n",
    "\n",
    "Notebook based on:\n",
    "\n",
    "0: Lopez de Prado, M. (2018). Advances in financial machine learning. John Wiley & Sons.\n",
    "\n",
    "https://www.amazon.com/Advances-Financial-Machine-Learning-Marcos/dp/1119482089\n",
    "\n",
    "1: AI4Finance Foundation\n",
    "\n",
    "https://github.com/AI4Finance-Foundation\n",
    "\n",
    "2: Optimal Trading Rules Detection with Triple Barrier Labeling\n",
    "\n",
    "https://www.youtube.com/watch?v=U2CxilKFue4\n",
    "\n",
    "3: Data Labelling, the Triple-barrier Method\n",
    "\n",
    "https://towardsdatascience.com/the-triple-barrier-method-251268419dcd\n",
    "\n",
    "\n",
    "4: Financial Machine Learning Part 1: Labels\n",
    "\n",
    "https://towardsdatascience.com/financial-machine-learning-part-1-labels-7eeed050f32e#:~:text=Adding%20Path%20Dependency%3A%20Triple%2DBarrier,%3A%20the%20triple%2Dbarrier%20method.\n",
    "\n",
    "\n",
    "5: Meta-Labeling: Solving for Non Stationarity and Position Sizing\n",
    "\n",
    "https://www.youtube.com/watch?v=WbgglcXfEzA\n",
    "\n",
    "\n",
    "6: Advances in Financial Machine Learning\n",
    "\n",
    "https://github.com/JackBrady/Financial-Machine-Learning/blob/master/USDJPY_Notebook.ipynb\n",
    "\n",
    "\n",
    "Specifically, important features for currently one coin at the time only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "93NXSRXxk5oh",
    "outputId": "ba319bea-aefa-4a5f-9a9a-b31e807767f8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/\n",
      "fatal: destination path 'FinRL-Meta' already exists and is not an empty directory.\n",
      "/FinRL-Meta\n",
      "Collecting git+https://github.com/AI4Finance-LLC/ElegantRL.git\n",
      "  Cloning https://github.com/AI4Finance-LLC/ElegantRL.git to /tmp/pip-req-build-cbjmohhf\n",
      "  Running command git clone -q https://github.com/AI4Finance-LLC/ElegantRL.git /tmp/pip-req-build-cbjmohhf\n",
      "Requirement already satisfied: gym in /usr/local/lib/python3.7/dist-packages (from elegantrl==0.3.3) (0.21.0)\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from elegantrl==0.3.3) (3.2.2)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from elegantrl==0.3.3) (1.21.6)\n",
      "Requirement already satisfied: pybullet in /usr/local/lib/python3.7/dist-packages (from elegantrl==0.3.3) (3.2.4)\n",
      "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from elegantrl==0.3.3) (1.11.0+cu113)\n",
      "Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from elegantrl==0.3.3) (4.1.2.30)\n",
      "Requirement already satisfied: box2d-py in /usr/local/lib/python3.7/dist-packages (from elegantrl==0.3.3) (2.3.8)\n",
      "Requirement already satisfied: importlib-metadata>=4.8.1 in /usr/local/lib/python3.7/dist-packages (from gym->elegantrl==0.3.3) (4.11.3)\n",
      "Requirement already satisfied: cloudpickle>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from gym->elegantrl==0.3.3) (1.3.0)\n",
      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.8.1->gym->elegantrl==0.3.3) (3.8.0)\n",
      "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.8.1->gym->elegantrl==0.3.3) (4.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->elegantrl==0.3.3) (0.11.0)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->elegantrl==0.3.3) (3.0.8)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->elegantrl==0.3.3) (2.8.2)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->elegantrl==0.3.3) (1.4.2)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->elegantrl==0.3.3) (1.15.0)\n",
      "Collecting git+https://github.com/AI4Finance-LLC/FinRL-Library.git\n",
      "  Cloning https://github.com/AI4Finance-LLC/FinRL-Library.git to /tmp/pip-req-build-rcmtlyew\n",
      "  Running command git clone -q https://github.com/AI4Finance-LLC/FinRL-Library.git /tmp/pip-req-build-rcmtlyew\n",
      "Collecting pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2\n",
      "  Cloning https://github.com/quantopian/pyfolio.git to /tmp/pip-install-p_k4ald4/pyfolio_cb7f12e8d9004bb2b7c0e5ca353ab623\n",
      "  Running command git clone -q https://github.com/quantopian/pyfolio.git /tmp/pip-install-p_k4ald4/pyfolio_cb7f12e8d9004bb2b7c0e5ca353ab623\n",
      "Collecting elegantrl@ git+https://github.com/AI4Finance-Foundation/ElegantRL.git#egg=elegantrl\n",
      "  Cloning https://github.com/AI4Finance-Foundation/ElegantRL.git to /tmp/pip-install-p_k4ald4/elegantrl_824a6112e59f47a8a9e7ac0c5336f3e9\n",
      "  Running command git clone -q https://github.com/AI4Finance-Foundation/ElegantRL.git /tmp/pip-install-p_k4ald4/elegantrl_824a6112e59f47a8a9e7ac0c5336f3e9\n",
      "Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.5) (1.21.6)\n",
      "Requirement already satisfied: pandas>=1.1.5 in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.5) (1.3.5)\n",
      "Requirement already satisfied: stockstats>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.5) (0.4.1)\n",
      "Requirement already satisfied: yfinance in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.5) (0.1.70)\n",
      "Requirement already satisfied: elegantrl in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.5) (0.3.3)\n",
      "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.5) (3.2.2)\n",
      "Requirement already satisfied: scikit-learn>=0.21.0 in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.5) (1.0.2)\n",
      "Requirement already satisfied: gym>=0.17 in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.5) (0.21.0)\n",
      "Requirement already satisfied: stable-baselines3[extra] in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.5) (1.5.0)\n",
      "Requirement already satisfied: ray[default] in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.5) (1.12.0)\n",
      "Requirement already satisfied: lz4 in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.5) (4.0.0)\n",
      "Requirement already satisfied: tensorboardX in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.5) (2.5)\n",
      "Requirement already satisfied: gputil in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.5) (1.4.0)\n",
      "Requirement already satisfied: pandas_market_calendars in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.5) (3.6.1)\n",
      "Requirement already satisfied: alpaca_trade_api in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.5) (2.1.0)\n",
      "Requirement already satisfied: ccxt>=1.66.32 in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.5) (1.72.98)\n",
      "Requirement already satisfied: jqdatasdk in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.5) (1.8.10)\n",
      "Requirement already satisfied: wrds in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.5) (3.1.1)\n",
      "Requirement already satisfied: pytest in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.5) (3.6.4)\n",
      "Requirement already satisfied: setuptools==59.5.0 in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.5) (59.5.0)\n",
      "Requirement already satisfied: wheel>=0.33.6 in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.5) (0.37.1)\n",
      "Requirement already satisfied: pre-commit in /usr/local/lib/python3.7/dist-packages (from finrl==0.3.5) (2.19.0)\n",
      "Requirement already satisfied: pybullet in /usr/local/lib/python3.7/dist-packages (from elegantrl@ git+https://github.com/AI4Finance-Foundation/ElegantRL.git#egg=elegantrl->finrl==0.3.5) (3.2.4)\n",
      "Requirement already satisfied: torch in /usr/local/lib/python3.7/dist-packages (from elegantrl@ git+https://github.com/AI4Finance-Foundation/ElegantRL.git#egg=elegantrl->finrl==0.3.5) (1.11.0+cu113)\n",
      "Requirement already satisfied: opencv-python in /usr/local/lib/python3.7/dist-packages (from elegantrl@ git+https://github.com/AI4Finance-Foundation/ElegantRL.git#egg=elegantrl->finrl==0.3.5) (4.1.2.30)\n",
      "Requirement already satisfied: box2d-py in /usr/local/lib/python3.7/dist-packages (from elegantrl@ git+https://github.com/AI4Finance-Foundation/ElegantRL.git#egg=elegantrl->finrl==0.3.5) (2.3.8)\n",
      "Requirement already satisfied: ipython>=3.2.3 in /usr/local/lib/python3.7/dist-packages (from pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (5.5.0)\n",
      "Requirement already satisfied: pytz>=2014.10 in /usr/local/lib/python3.7/dist-packages (from pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (2022.1)\n",
      "Requirement already satisfied: scipy>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (1.4.1)\n",
      "Requirement already satisfied: seaborn>=0.7.1 in /usr/local/lib/python3.7/dist-packages (from pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (0.11.2)\n",
      "Requirement already satisfied: empyrical>=0.5.0 in /usr/local/lib/python3.7/dist-packages (from pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (0.5.5)\n",
      "Requirement already satisfied: certifi>=2018.1.18 in /usr/local/lib/python3.7/dist-packages (from ccxt>=1.66.32->finrl==0.3.5) (2021.10.8)\n",
      "Requirement already satisfied: yarl==1.7.2 in /usr/local/lib/python3.7/dist-packages (from ccxt>=1.66.32->finrl==0.3.5) (1.7.2)\n",
      "Requirement already satisfied: cryptography>=2.6.1 in /usr/local/lib/python3.7/dist-packages (from ccxt>=1.66.32->finrl==0.3.5) (37.0.2)\n",
      "Requirement already satisfied: aiohttp>=3.8 in /usr/local/lib/python3.7/dist-packages (from ccxt>=1.66.32->finrl==0.3.5) (3.8.1)\n",
      "Requirement already satisfied: requests>=2.18.4 in /usr/local/lib/python3.7/dist-packages (from ccxt>=1.66.32->finrl==0.3.5) (2.27.1)\n",
      "Requirement already satisfied: aiodns>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from ccxt>=1.66.32->finrl==0.3.5) (3.0.0)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from yarl==1.7.2->ccxt>=1.66.32->finrl==0.3.5) (4.2.0)\n",
      "Requirement already satisfied: idna>=2.0 in /usr/local/lib/python3.7/dist-packages (from yarl==1.7.2->ccxt>=1.66.32->finrl==0.3.5) (2.10)\n",
      "Requirement already satisfied: multidict>=4.0 in /usr/local/lib/python3.7/dist-packages (from yarl==1.7.2->ccxt>=1.66.32->finrl==0.3.5) (6.0.2)\n",
      "Requirement already satisfied: pycares>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from aiodns>=1.1.1->ccxt>=1.66.32->finrl==0.3.5) (4.1.2)\n",
      "Requirement already satisfied: asynctest==0.13.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp>=3.8->ccxt>=1.66.32->finrl==0.3.5) (0.13.0)\n",
      "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp>=3.8->ccxt>=1.66.32->finrl==0.3.5) (2.0.12)\n",
      "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp>=3.8->ccxt>=1.66.32->finrl==0.3.5) (21.4.0)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.7/dist-packages (from aiohttp>=3.8->ccxt>=1.66.32->finrl==0.3.5) (1.2.0)\n",
      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.7/dist-packages (from aiohttp>=3.8->ccxt>=1.66.32->finrl==0.3.5) (4.0.2)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from aiohttp>=3.8->ccxt>=1.66.32->finrl==0.3.5) (1.3.0)\n",
      "Requirement already satisfied: cffi>=1.12 in /usr/local/lib/python3.7/dist-packages (from cryptography>=2.6.1->ccxt>=1.66.32->finrl==0.3.5) (1.15.0)\n",
      "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.12->cryptography>=2.6.1->ccxt>=1.66.32->finrl==0.3.5) (2.21)\n",
      "Requirement already satisfied: pandas-datareader>=0.2 in /usr/local/lib/python3.7/dist-packages (from empyrical>=0.5.0->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (0.9.0)\n",
      "Requirement already satisfied: cloudpickle>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from gym>=0.17->finrl==0.3.5) (1.3.0)\n",
      "Requirement already satisfied: importlib-metadata>=4.8.1 in /usr/local/lib/python3.7/dist-packages (from gym>=0.17->finrl==0.3.5) (4.11.3)\n",
      "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=4.8.1->gym>=0.17->finrl==0.3.5) (3.8.0)\n",
      "Requirement already satisfied: pygments in /usr/local/lib/python3.7/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (2.6.1)\n",
      "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.7/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (0.8.1)\n",
      "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.7/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (5.1.1)\n",
      "Requirement already satisfied: pickleshare in /usr/local/lib/python3.7/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (0.7.5)\n",
      "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.7/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (1.0.18)\n",
      "Requirement already satisfied: decorator in /usr/local/lib/python3.7/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (4.4.2)\n",
      "Requirement already satisfied: pexpect in /usr/local/lib/python3.7/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (4.8.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->finrl==0.3.5) (0.11.0)\n",
      "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->finrl==0.3.5) (2.8.2)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->finrl==0.3.5) (1.4.2)\n",
      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->finrl==0.3.5) (3.0.8)\n",
      "Requirement already satisfied: lxml in /usr/local/lib/python3.7/dist-packages (from pandas-datareader>=0.2->empyrical>=0.5.0->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (4.8.0)\n",
      "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (0.2.5)\n",
      "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (1.15.0)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.18.4->ccxt>=1.66.32->finrl==0.3.5) (1.24.3)\n",
      "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.0->finrl==0.3.5) (1.1.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.21.0->finrl==0.3.5) (3.1.0)\n",
      "Requirement already satisfied: deprecation==2.1.0 in /usr/local/lib/python3.7/dist-packages (from alpaca_trade_api->finrl==0.3.5) (2.1.0)\n",
      "Requirement already satisfied: msgpack==1.0.3 in /usr/local/lib/python3.7/dist-packages (from alpaca_trade_api->finrl==0.3.5) (1.0.3)\n",
      "Requirement already satisfied: websocket-client<2,>=0.56.0 in /usr/local/lib/python3.7/dist-packages (from alpaca_trade_api->finrl==0.3.5) (1.3.2)\n",
      "Requirement already satisfied: PyYAML==6.0 in /usr/local/lib/python3.7/dist-packages (from alpaca_trade_api->finrl==0.3.5) (6.0)\n",
      "Requirement already satisfied: websockets<11,>=9.0 in /usr/local/lib/python3.7/dist-packages (from alpaca_trade_api->finrl==0.3.5) (10.3)\n",
      "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from deprecation==2.1.0->alpaca_trade_api->finrl==0.3.5) (21.3)\n",
      "Requirement already satisfied: pyluach in /usr/local/lib/python3.7/dist-packages (from pandas_market_calendars->finrl==0.3.5) (1.4.1)\n",
      "Requirement already satisfied: korean-lunar-calendar in /usr/local/lib/python3.7/dist-packages (from pandas_market_calendars->finrl==0.3.5) (0.2.1)\n",
      "Requirement already satisfied: toolz in /usr/local/lib/python3.7/dist-packages (from pandas_market_calendars->finrl==0.3.5) (0.11.2)\n",
      "Requirement already satisfied: thriftpy2>=0.3.9 in /usr/local/lib/python3.7/dist-packages (from jqdatasdk->finrl==0.3.5) (0.4.14)\n",
      "Requirement already satisfied: pymysql>=0.7.6 in /usr/local/lib/python3.7/dist-packages (from jqdatasdk->finrl==0.3.5) (1.0.2)\n",
      "Requirement already satisfied: SQLAlchemy>=1.2.8 in /usr/local/lib/python3.7/dist-packages (from jqdatasdk->finrl==0.3.5) (1.4.36)\n",
      "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.7/dist-packages (from SQLAlchemy>=1.2.8->jqdatasdk->finrl==0.3.5) (1.1.2)\n",
      "Requirement already satisfied: ply<4.0,>=3.4 in /usr/local/lib/python3.7/dist-packages (from thriftpy2>=0.3.9->jqdatasdk->finrl==0.3.5) (3.11)\n",
      "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.7/dist-packages (from pexpect->ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.3.5) (0.7.0)\n",
      "Requirement already satisfied: cfgv>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from pre-commit->finrl==0.3.5) (3.3.1)\n",
      "Requirement already satisfied: virtualenv>=20.0.8 in /usr/local/lib/python3.7/dist-packages (from pre-commit->finrl==0.3.5) (20.14.1)\n",
      "Requirement already satisfied: nodeenv>=0.11.1 in /usr/local/lib/python3.7/dist-packages (from pre-commit->finrl==0.3.5) (1.6.0)\n",
      "Requirement already satisfied: identify>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from pre-commit->finrl==0.3.5) (2.5.0)\n",
      "Requirement already satisfied: toml in /usr/local/lib/python3.7/dist-packages (from pre-commit->finrl==0.3.5) (0.10.2)\n",
      "Requirement already satisfied: platformdirs<3,>=2 in /usr/local/lib/python3.7/dist-packages (from virtualenv>=20.0.8->pre-commit->finrl==0.3.5) (2.5.2)\n",
      "Requirement already satisfied: distlib<1,>=0.3.1 in /usr/local/lib/python3.7/dist-packages (from virtualenv>=20.0.8->pre-commit->finrl==0.3.5) (0.3.4)\n",
      "Requirement already satisfied: filelock<4,>=3.2 in /usr/local/lib/python3.7/dist-packages (from virtualenv>=20.0.8->pre-commit->finrl==0.3.5) (3.6.0)\n",
      "Requirement already satisfied: more-itertools>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from pytest->finrl==0.3.5) (8.12.0)\n",
      "Requirement already satisfied: pluggy<0.8,>=0.5 in /usr/local/lib/python3.7/dist-packages (from pytest->finrl==0.3.5) (0.7.1)\n",
      "Requirement already satisfied: atomicwrites>=1.0 in /usr/local/lib/python3.7/dist-packages (from pytest->finrl==0.3.5) (1.4.0)\n",
      "Requirement already satisfied: py>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from pytest->finrl==0.3.5) (1.11.0)\n",
      "Requirement already satisfied: jsonschema in /usr/local/lib/python3.7/dist-packages (from ray[default]->finrl==0.3.5) (4.3.3)\n",
      "Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.7/dist-packages (from ray[default]->finrl==0.3.5) (7.1.2)\n",
      "Requirement already satisfied: protobuf>=3.15.3 in /usr/local/lib/python3.7/dist-packages (from ray[default]->finrl==0.3.5) (3.17.3)\n",
      "Requirement already satisfied: grpcio<=1.43.0,>=1.28.1 in /usr/local/lib/python3.7/dist-packages (from ray[default]->finrl==0.3.5) (1.43.0)\n",
      "Requirement already satisfied: aiohttp-cors in /usr/local/lib/python3.7/dist-packages (from ray[default]->finrl==0.3.5) (0.7.0)\n",
      "Requirement already satisfied: colorful in /usr/local/lib/python3.7/dist-packages (from ray[default]->finrl==0.3.5) (0.5.4)\n",
      "Requirement already satisfied: prometheus-client<0.14.0,>=0.7.1 in /usr/local/lib/python3.7/dist-packages (from ray[default]->finrl==0.3.5) (0.13.1)\n",
      "Requirement already satisfied: opencensus in /usr/local/lib/python3.7/dist-packages (from ray[default]->finrl==0.3.5) (0.9.0)\n",
      "Requirement already satisfied: py-spy>=0.2.0 in /usr/local/lib/python3.7/dist-packages (from ray[default]->finrl==0.3.5) (0.3.11)\n",
      "Requirement already satisfied: smart-open in /usr/local/lib/python3.7/dist-packages (from ray[default]->finrl==0.3.5) (6.0.0)\n",
      "Requirement already satisfied: gpustat>=1.0.0b1 in /usr/local/lib/python3.7/dist-packages (from ray[default]->finrl==0.3.5) (1.0.0b1)\n",
      "Requirement already satisfied: nvidia-ml-py3>=7.352.0 in /usr/local/lib/python3.7/dist-packages (from gpustat>=1.0.0b1->ray[default]->finrl==0.3.5) (7.352.0)\n",
      "Requirement already satisfied: blessed>=1.17.1 in /usr/local/lib/python3.7/dist-packages (from gpustat>=1.0.0b1->ray[default]->finrl==0.3.5) (1.19.1)\n",
      "Requirement already satisfied: psutil in /usr/local/lib/python3.7/dist-packages (from gpustat>=1.0.0b1->ray[default]->finrl==0.3.5) (5.4.8)\n",
      "Requirement already satisfied: importlib-resources>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema->ray[default]->finrl==0.3.5) (5.7.1)\n",
      "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.7/dist-packages (from jsonschema->ray[default]->finrl==0.3.5) (0.18.1)\n",
      "Requirement already satisfied: opencensus-context>=0.1.2 in /usr/local/lib/python3.7/dist-packages (from opencensus->ray[default]->finrl==0.3.5) (0.1.2)\n",
      "Requirement already satisfied: google-api-core<3.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from opencensus->ray[default]->finrl==0.3.5) (1.31.5)\n",
      "Requirement already satisfied: google-auth<2.0dev,>=1.25.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<3.0.0,>=1.0.0->opencensus->ray[default]->finrl==0.3.5) (1.35.0)\n",
      "Requirement already satisfied: googleapis-common-protos<2.0dev,>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from google-api-core<3.0.0,>=1.0.0->opencensus->ray[default]->finrl==0.3.5) (1.56.0)\n",
      "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<2.0dev,>=1.25.0->google-api-core<3.0.0,>=1.0.0->opencensus->ray[default]->finrl==0.3.5) (4.8)\n",
      "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2.0dev,>=1.25.0->google-api-core<3.0.0,>=1.0.0->opencensus->ray[default]->finrl==0.3.5) (0.2.8)\n",
      "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2.0dev,>=1.25.0->google-api-core<3.0.0,>=1.0.0->opencensus->ray[default]->finrl==0.3.5) (4.2.4)\n",
      "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2.0dev,>=1.25.0->google-api-core<3.0.0,>=1.0.0->opencensus->ray[default]->finrl==0.3.5) (0.4.8)\n",
      "Requirement already satisfied: tabulate in /usr/local/lib/python3.7/dist-packages (from ray[default]->finrl==0.3.5) (0.8.9)\n",
      "Requirement already satisfied: ale-py~=0.7.4 in /usr/local/lib/python3.7/dist-packages (from stable-baselines3[extra]->finrl==0.3.5) (0.7.5)\n",
      "Requirement already satisfied: tensorboard>=2.2.0 in /usr/local/lib/python3.7/dist-packages (from stable-baselines3[extra]->finrl==0.3.5) (2.8.0)\n",
      "Requirement already satisfied: pillow in /usr/local/lib/python3.7/dist-packages (from stable-baselines3[extra]->finrl==0.3.5) (7.1.2)\n",
      "Requirement already satisfied: autorom[accept-rom-license]~=0.4.2 in /usr/local/lib/python3.7/dist-packages (from stable-baselines3[extra]->finrl==0.3.5) (0.4.2)\n",
      "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from autorom[accept-rom-license]~=0.4.2->stable-baselines3[extra]->finrl==0.3.5) (4.64.0)\n",
      "Requirement already satisfied: AutoROM.accept-rom-license in /usr/local/lib/python3.7/dist-packages (from autorom[accept-rom-license]~=0.4.2->stable-baselines3[extra]->finrl==0.3.5) (0.4.2)\n",
      "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->stable-baselines3[extra]->finrl==0.3.5) (0.4.6)\n",
      "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->stable-baselines3[extra]->finrl==0.3.5) (1.0.0)\n",
      "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->stable-baselines3[extra]->finrl==0.3.5) (3.3.6)\n",
      "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->stable-baselines3[extra]->finrl==0.3.5) (0.6.1)\n",
      "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->stable-baselines3[extra]->finrl==0.3.5) (1.8.1)\n",
      "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.2.0->stable-baselines3[extra]->finrl==0.3.5) (1.0.1)\n",
      "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0->stable-baselines3[extra]->finrl==0.3.5) (1.3.1)\n",
      "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.2.0->stable-baselines3[extra]->finrl==0.3.5) (3.2.0)\n",
      "Requirement already satisfied: psycopg2-binary in /usr/local/lib/python3.7/dist-packages (from wrds->finrl==0.3.5) (2.9.3)\n",
      "Requirement already satisfied: mock in /usr/local/lib/python3.7/dist-packages (from wrds->finrl==0.3.5) (4.0.3)\n",
      "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from yfinance->finrl==0.3.5) (0.0.10)\n",
      "Requirement already satisfied: gputil in /usr/local/lib/python3.7/dist-packages (1.4.0)\n",
      "Requirement already satisfied: trading_calendars in /usr/local/lib/python3.7/dist-packages (2.1.1)\n",
      "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from trading_calendars) (2.8.2)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from trading_calendars) (1.21.6)\n",
      "Requirement already satisfied: toolz in /usr/local/lib/python3.7/dist-packages (from trading_calendars) (0.11.2)\n",
      "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from trading_calendars) (2022.1)\n",
      "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from trading_calendars) (1.3.5)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from trading_calendars) (1.15.0)\n",
      "Requirement already satisfied: python-binance in /usr/local/lib/python3.7/dist-packages (1.0.16)\n",
      "Requirement already satisfied: websockets in /usr/local/lib/python3.7/dist-packages (from python-binance) (10.3)\n",
      "Requirement already satisfied: dateparser in /usr/local/lib/python3.7/dist-packages (from python-binance) (1.1.1)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from python-binance) (2.27.1)\n",
      "Requirement already satisfied: ujson in /usr/local/lib/python3.7/dist-packages (from python-binance) (5.2.0)\n",
      "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from python-binance) (1.15.0)\n",
      "Requirement already satisfied: aiohttp in /usr/local/lib/python3.7/dist-packages (from python-binance) (3.8.1)\n",
      "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.7/dist-packages (from aiohttp->python-binance) (6.0.2)\n",
      "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from aiohttp->python-binance) (1.3.0)\n",
      "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->python-binance) (1.7.2)\n",
      "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.7/dist-packages (from aiohttp->python-binance) (1.2.0)\n",
      "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->python-binance) (21.4.0)\n",
      "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->python-binance) (2.0.12)\n",
      "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.7/dist-packages (from aiohttp->python-binance) (4.0.2)\n",
      "Requirement already satisfied: asynctest==0.13.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp->python-binance) (0.13.0)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from aiohttp->python-binance) (4.2.0)\n",
      "Requirement already satisfied: idna>=2.0 in /usr/local/lib/python3.7/dist-packages (from yarl<2.0,>=1.0->aiohttp->python-binance) (2.10)\n",
      "Requirement already satisfied: python-dateutil in /usr/local/lib/python3.7/dist-packages (from dateparser->python-binance) (2.8.2)\n",
      "Requirement already satisfied: pytz in /usr/local/lib/python3.7/dist-packages (from dateparser->python-binance) (2022.1)\n",
      "Requirement already satisfied: regex!=2019.02.19,!=2021.8.27,<2022.3.15 in /usr/local/lib/python3.7/dist-packages (from dateparser->python-binance) (2019.12.20)\n",
      "Requirement already satisfied: tzlocal in /usr/local/lib/python3.7/dist-packages (from dateparser->python-binance) (1.5.1)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->python-binance) (2021.10.8)\n",
      "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->python-binance) (1.24.3)\n"
     ]
    }
   ],
   "source": [
    "# Install required packages\n",
    "\n",
    "%cd /\n",
    "!pip install wrds\n",
    "!pip install swig\n",
    "!git clone https://github.com/AI4Finance-Foundation/FinRL-Meta\n",
    "%cd /FinRL-Meta/\n",
    "!pip install git+https://github.com/AI4Finance-Foundation/ElegantRL.git\n",
    "!pip install -q condacolab\n",
    "import condacolab\n",
    "condacolab.install()\n",
    "!apt-get update -y -qq && apt-get install -y -qq cmake libopenmpi-dev python3-dev zlib1g-dev libgl1-mesa-glx swig\n",
    "!pip install git+https://github.com/AI4Finance-Foundation/FinRL.git\n",
    "!pip install gputil\n",
    "!pip install trading_calendars\n",
    "!pip install python-binance\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LCumXUTo22tu",
    "outputId": "f5777aa9-db9f-4995-ce5a-70ab89b8101e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-05-08 13:00:14--  http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz\n",
      "Resolving prdownloads.sourceforge.net (prdownloads.sourceforge.net)... 204.68.111.105\n",
      "Connecting to prdownloads.sourceforge.net (prdownloads.sourceforge.net)|204.68.111.105|:80... connected.\n",
      "HTTP request sent, awaiting response... 301 Moved Permanently\n",
      "Location: http://downloads.sourceforge.net/project/ta-lib/ta-lib/0.4.0/ta-lib-0.4.0-src.tar.gz [following]\n",
      "--2022-05-08 13:00:17--  http://downloads.sourceforge.net/project/ta-lib/ta-lib/0.4.0/ta-lib-0.4.0-src.tar.gz\n",
      "Resolving downloads.sourceforge.net (downloads.sourceforge.net)... 204.68.111.105\n",
      "Reusing existing connection to prdownloads.sourceforge.net:80.\n",
      "HTTP request sent, awaiting response... 302 Found\n",
      "Location: http://nchc.dl.sourceforge.net/project/ta-lib/ta-lib/0.4.0/ta-lib-0.4.0-src.tar.gz [following]\n",
      "--2022-05-08 13:00:18--  http://nchc.dl.sourceforge.net/project/ta-lib/ta-lib/0.4.0/ta-lib-0.4.0-src.tar.gz\n",
      "Resolving nchc.dl.sourceforge.net (nchc.dl.sourceforge.net)... 140.110.96.69, 2001:e10:ffff:1f02::17\n",
      "Connecting to nchc.dl.sourceforge.net (nchc.dl.sourceforge.net)|140.110.96.69|:80... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 1330299 (1.3M) [application/x-gzip]\n",
      "Saving to: ‘ta-lib-0.4.0-src.tar.gz.2’\n",
      "\n",
      "ta-lib-0.4.0-src.ta 100%[===================>]   1.27M  --.-KB/s    in 0.07s   \n",
      "\n",
      "2022-05-08 13:00:18 (18.8 MB/s) - ‘ta-lib-0.4.0-src.tar.gz.2’ saved [1330299/1330299]\n",
      "\n",
      "ta-lib/\n",
      "ta-lib/config.sub\n",
      "ta-lib/aclocal.m4\n",
      "ta-lib/CHANGELOG.TXT\n",
      "ta-lib/include/\n",
      "ta-lib/include/ta_abstract.h\n",
      "ta-lib/include/ta_func.h\n",
      "ta-lib/include/ta_common.h\n",
      "ta-lib/include/ta_config.h.in\n",
      "ta-lib/include/Makefile.am\n",
      "ta-lib/include/ta_libc.h\n",
      "ta-lib/include/ta_defs.h\n",
      "ta-lib/missing\n",
      "ta-lib/ta-lib.spec.in\n",
      "ta-lib/config.guess\n",
      "ta-lib/Makefile.in\n",
      "ta-lib/ta-lib.dpkg.in\n",
      "ta-lib/Makefile.am\n",
      "ta-lib/autogen.sh\n",
      "ta-lib/install-sh\n",
      "ta-lib/configure\n",
      "ta-lib/depcomp\n",
      "ta-lib/HISTORY.TXT\n",
      "ta-lib/configure.in\n",
      "ta-lib/autom4te.cache/\n",
      "ta-lib/autom4te.cache/output.0\n",
      "ta-lib/autom4te.cache/requests\n",
      "ta-lib/autom4te.cache/output.1\n",
      "ta-lib/autom4te.cache/traces.0\n",
      "ta-lib/autom4te.cache/traces.1\n",
      "ta-lib/ltmain.sh\n",
      "ta-lib/ta-lib-config.in\n",
      "ta-lib/src/\n",
      "ta-lib/src/ta_func/\n",
      "ta-lib/src/ta_func/ta_MACDFIX.c\n",
      "ta-lib/src/ta_func/ta_CDLPIERCING.c\n",
      "ta-lib/src/ta_func/ta_DIV.c\n",
      "ta-lib/src/ta_func/ta_ROCR100.c\n",
      "ta-lib/src/ta_func/ta_ADXR.c\n",
      "ta-lib/src/ta_func/ta_MAVP.c\n",
      "ta-lib/src/ta_func/ta_CDLCLOSINGMARUBOZU.c\n",
      "ta-lib/src/ta_func/ta_COSH.c\n",
      "ta-lib/src/ta_func/ta_EXP.c\n",
      "ta-lib/src/ta_func/ta_MINMAXINDEX.c\n",
      "ta-lib/src/ta_func/ta_SQRT.c\n",
      "ta-lib/src/ta_func/ta_FLOOR.c\n",
      "ta-lib/src/ta_func/ta_CDLCONCEALBABYSWALL.c\n",
      "ta-lib/src/ta_func/ta_NATR.c\n",
      "ta-lib/src/ta_func/ta_CDLHARAMICROSS.c\n",
      "ta-lib/src/ta_func/ta_MINUS_DM.c\n",
      "ta-lib/src/ta_func/ta_LOG10.c\n",
      "ta-lib/src/ta_func/ta_LINEARREG_ANGLE.c\n",
      "ta-lib/src/ta_func/ta_RSI.c\n",
      "ta-lib/src/ta_func/ta_CDLABANDONEDBABY.c\n",
      "ta-lib/src/ta_func/ta_SAR.c\n",
      "ta-lib/src/ta_func/ta_CDLBREAKAWAY.c\n",
      "ta-lib/src/ta_func/ta_CDLDRAGONFLYDOJI.c\n",
      "ta-lib/src/ta_func/ta_CDLHIGHWAVE.c\n",
      "ta-lib/src/ta_func/ta_CDLKICKING.c\n",
      "ta-lib/src/ta_func/ta_CDLDOJISTAR.c\n",
      "ta-lib/src/ta_func/ta_VAR.c\n",
      "ta-lib/src/ta_func/ta_CDLMATCHINGLOW.c\n",
      "ta-lib/src/ta_func/ta_CDLGAPSIDESIDEWHITE.c\n",
      "ta-lib/src/ta_func/ta_CDLMARUBOZU.c\n",
      "ta-lib/src/ta_func/ta_AROONOSC.c\n",
      "ta-lib/src/ta_func/ta_WCLPRICE.c\n",
      "ta-lib/src/ta_func/ta_CDLEVENINGDOJISTAR.c\n",
      "ta-lib/src/ta_func/ta_CDL3INSIDE.c\n",
      "ta-lib/src/ta_func/ta_OBV.c\n",
      "ta-lib/src/ta_func/ta_AROON.c\n",
      "ta-lib/src/ta_func/ta_CDLBELTHOLD.c\n",
      "ta-lib/src/ta_func/ta_CDLSPINNINGTOP.c\n",
      "ta-lib/src/ta_func/ta_AD.c\n",
      "ta-lib/src/ta_func/ta_MAX.c\n",
      "ta-lib/src/ta_func/ta_CDLENGULFING.c\n",
      "ta-lib/src/ta_func/ta_MINMAX.c\n",
      "ta-lib/src/ta_func/ta_CDLINNECK.c\n",
      "ta-lib/src/ta_func/ta_STDDEV.c\n",
      "ta-lib/src/ta_func/ta_NVI.c\n",
      "ta-lib/src/ta_func/ta_CDLHAMMER.c\n",
      "ta-lib/src/ta_func/ta_ASIN.c\n",
      "ta-lib/src/ta_func/ta_SUM.c\n",
      "ta-lib/src/ta_func/ta_STOCH.c\n",
      "ta-lib/src/ta_func/ta_CDLLONGLEGGEDDOJI.c\n",
      "ta-lib/src/ta_func/ta_MEDPRICE.c\n",
      "ta-lib/src/ta_func/ta_CDL3STARSINSOUTH.c\n",
      "ta-lib/src/ta_func/ta_HT_TRENDMODE.c\n",
      "ta-lib/src/ta_func/ta_BBANDS.c\n",
      "ta-lib/src/ta_func/ta_CDLMORNINGSTAR.c\n",
      "ta-lib/src/ta_func/ta_HT_DCPHASE.c\n",
      "ta-lib/src/ta_func/ta_CDLLONGLINE.c\n",
      "ta-lib/src/ta_func/ta_TAN.c\n",
      "ta-lib/src/ta_func/ta_SMA.c\n",
      "ta-lib/src/ta_func/ta_DX.c\n",
      "ta-lib/src/ta_func/ta_MIDPOINT.c\n",
      "ta-lib/src/ta_func/ta_CDL2CROWS.c\n",
      "ta-lib/src/ta_func/ta_CORREL.c\n",
      "ta-lib/src/ta_func/ta_CDL3BLACKCROWS.c\n",
      "ta-lib/src/ta_func/ta_ADD.c\n",
      "ta-lib/src/ta_func/Makefile.in\n",
      "ta-lib/src/ta_func/ta_CDLTHRUSTING.c\n",
      "ta-lib/src/ta_func/ta_SUB.c\n",
      "ta-lib/src/ta_func/ta_CDLSTALLEDPATTERN.c\n",
      "ta-lib/src/ta_func/ta_CDLTRISTAR.c\n",
      "ta-lib/src/ta_func/ta_MA.c\n",
      "ta-lib/src/ta_func/ta_HT_SINE.c\n",
      "ta-lib/src/ta_func/ta_ACOS.c\n",
      "ta-lib/src/ta_func/ta_CDLSTICKSANDWICH.c\n",
      "ta-lib/src/ta_func/ta_SINH.c\n",
      "ta-lib/src/ta_func/ta_utility.h\n",
      "ta-lib/src/ta_func/ta_CDLSHORTLINE.c\n",
      "ta-lib/src/ta_func/ta_ATAN.c\n",
      "ta-lib/src/ta_func/ta_CDLADVANCEBLOCK.c\n",
      "ta-lib/src/ta_func/ta_CDLKICKINGBYLENGTH.c\n",
      "ta-lib/src/ta_func/ta_CDLSHOOTINGSTAR.c\n",
      "ta-lib/src/ta_func/ta_ROCR.c\n",
      "ta-lib/src/ta_func/ta_WMA.c\n",
      "ta-lib/src/ta_func/ta_CDLDARKCLOUDCOVER.c\n",
      "ta-lib/src/ta_func/ta_CDLXSIDEGAP3METHODS.c\n",
      "ta-lib/src/ta_func/ta_TYPPRICE.c\n",
      "ta-lib/src/ta_func/ta_CDL3WHITESOLDIERS.c\n",
      "ta-lib/src/ta_func/Makefile.am\n",
      "ta-lib/src/ta_func/ta_MACDEXT.c\n",
      "ta-lib/src/ta_func/ta_ADX.c\n",
      "ta-lib/src/ta_func/ta_PLUS_DM.c\n",
      "ta-lib/src/ta_func/ta_CDLUPSIDEGAP2CROWS.c\n",
      "ta-lib/src/ta_func/ta_LN.c\n",
      "ta-lib/src/ta_func/ta_DEMA.c\n",
      "ta-lib/src/ta_func/ta_CDL3OUTSIDE.c\n",
      "ta-lib/src/ta_func/ta_CDLTASUKIGAP.c\n",
      "ta-lib/src/ta_func/ta_MAMA.c\n",
      "ta-lib/src/ta_func/ta_CDLMORNINGDOJISTAR.c\n",
      "ta-lib/src/ta_func/ta_PLUS_DI.c\n",
      "ta-lib/src/ta_func/ta_MININDEX.c\n",
      "ta-lib/src/ta_func/ta_COS.c\n",
      "ta-lib/src/ta_func/ta_HT_TRENDLINE.c\n",
      "ta-lib/src/ta_func/ta_MIDPRICE.c\n",
      "ta-lib/src/ta_func/ta_CEIL.c\n",
      "ta-lib/src/ta_func/ta_TRIMA.c\n",
      "ta-lib/src/ta_func/ta_CDLSEPARATINGLINES.c\n",
      "ta-lib/src/ta_func/ta_ROCP.c\n",
      "ta-lib/src/ta_func/ta_CDLHOMINGPIGEON.c\n",
      "ta-lib/src/ta_func/ta_CDLHANGINGMAN.c\n",
      "ta-lib/src/ta_func/ta_AVGPRICE.c\n",
      "ta-lib/src/ta_func/ta_APO.c\n",
      "ta-lib/src/ta_func/ta_CDLRISEFALL3METHODS.c\n",
      "ta-lib/src/ta_func/ta_TRANGE.c\n",
      "ta-lib/src/ta_func/ta_TSF.c\n",
      "ta-lib/src/ta_func/ta_LINEARREG.c\n",
      "ta-lib/src/ta_func/ta_PVI.c\n",
      "ta-lib/src/ta_func/ta_CDLHIKKAKEMOD.c\n",
      "ta-lib/src/ta_func/ta_MFI.c\n",
      "ta-lib/src/ta_func/ta_CDLHARAMI.c\n",
      "ta-lib/src/ta_func/ta_MACD.c\n",
      "ta-lib/src/ta_func/ta_BETA.c\n",
      "ta-lib/src/ta_func/ta_CDLINVERTEDHAMMER.c\n",
      "ta-lib/src/ta_func/ta_LINEARREG_SLOPE.c\n",
      "ta-lib/src/ta_func/ta_STOCHF.c\n",
      "ta-lib/src/ta_func/ta_MIN.c\n",
      "ta-lib/src/ta_func/ta_CDLIDENTICAL3CROWS.c\n",
      "ta-lib/src/ta_func/ta_CDLRICKSHAWMAN.c\n",
      "ta-lib/src/ta_func/ta_T3.c\n",
      "ta-lib/src/ta_func/ta_CDLMATHOLD.c\n",
      "ta-lib/src/ta_func/ta_CDLUNIQUE3RIVER.c\n",
      "ta-lib/src/ta_func/ta_ADOSC.c\n",
      "ta-lib/src/ta_func/ta_MAXINDEX.c\n",
      "ta-lib/src/ta_func/ta_ULTOSC.c\n",
      "ta-lib/src/ta_func/ta_TRIX.c\n",
      "ta-lib/src/ta_func/ta_MOM.c\n",
      "ta-lib/src/ta_func/ta_CDLDOJI.c\n",
      "ta-lib/src/ta_func/ta_EMA.c\n",
      "ta-lib/src/ta_func/ta_STOCHRSI.c\n",
      "ta-lib/src/ta_func/ta_ROC.c\n",
      "ta-lib/src/ta_func/ta_CDLEVENINGSTAR.c\n",
      "ta-lib/src/ta_func/ta_CDLCOUNTERATTACK.c\n",
      "ta-lib/src/ta_func/ta_LINEARREG_INTERCEPT.c\n",
      "ta-lib/src/ta_func/ta_SAREXT.c\n",
      "ta-lib/src/ta_func/ta_WILLR.c\n",
      "ta-lib/src/ta_func/ta_MULT.c\n",
      "ta-lib/src/ta_func/ta_ATR.c\n",
      "ta-lib/src/ta_func/ta_BOP.c\n",
      "ta-lib/src/ta_func/ta_CMO.c\n",
      "ta-lib/src/ta_func/ta_CDLONNECK.c\n",
      "ta-lib/src/ta_func/ta_CCI.c\n",
      "ta-lib/src/ta_func/ta_CDLLADDERBOTTOM.c\n",
      "ta-lib/src/ta_func/ta_HT_PHASOR.c\n",
      "ta-lib/src/ta_func/ta_utility.c\n",
      "ta-lib/src/ta_func/ta_PPO.c\n",
      "ta-lib/src/ta_func/ta_CDLHIKKAKE.c\n",
      "ta-lib/src/ta_func/ta_HT_DCPERIOD.c\n",
      "ta-lib/src/ta_func/ta_CDL3LINESTRIKE.c\n",
      "ta-lib/src/ta_func/ta_TEMA.c\n",
      "ta-lib/src/ta_func/ta_SIN.c\n",
      "ta-lib/src/ta_func/ta_MINUS_DI.c\n",
      "ta-lib/src/ta_func/ta_KAMA.c\n",
      "ta-lib/src/ta_func/ta_TANH.c\n",
      "ta-lib/src/ta_func/ta_CDLTAKURI.c\n",
      "ta-lib/src/ta_func/ta_CDLGRAVESTONEDOJI.c\n",
      "ta-lib/src/ta_common/\n",
      "ta-lib/src/ta_common/ta_pragma.h\n",
      "ta-lib/src/ta_common/ta_magic_nb.h\n",
      "ta-lib/src/ta_common/ta_retcode.csv\n",
      "ta-lib/src/ta_common/Makefile.in\n",
      "ta-lib/src/ta_common/Makefile.am\n",
      "ta-lib/src/ta_common/ta_memory.h\n",
      "ta-lib/src/ta_common/ta_version.c\n",
      "ta-lib/src/ta_common/ta_global.h\n",
      "ta-lib/src/ta_common/ta_global.c\n",
      "ta-lib/src/ta_common/ta_retcode.c\n",
      "ta-lib/src/Makefile.in\n",
      "ta-lib/src/ta_abstract/\n",
      "ta-lib/src/ta_abstract/frames/\n",
      "ta-lib/src/ta_abstract/frames/ta_frame.c\n",
      "ta-lib/src/ta_abstract/frames/ta_frame.h\n",
      "ta-lib/src/ta_abstract/excel_glue.c\n",
      "ta-lib/src/ta_abstract/ta_frame_priv.h\n",
      "ta-lib/src/ta_abstract/ta_func_api.c\n",
      "ta-lib/src/ta_abstract/Makefile.in\n",
      "ta-lib/src/ta_abstract/ta_def_ui.h\n",
      "ta-lib/src/ta_abstract/Makefile.am\n",
      "ta-lib/src/ta_abstract/ta_abstract.c\n",
      "ta-lib/src/ta_abstract/ta_group_idx.c\n",
      "ta-lib/src/ta_abstract/tables/\n",
      "ta-lib/src/ta_abstract/tables/table_u.c\n",
      "ta-lib/src/ta_abstract/tables/table_x.c\n",
      "ta-lib/src/ta_abstract/tables/table_r.c\n",
      "ta-lib/src/ta_abstract/tables/table_f.c\n",
      "ta-lib/src/ta_abstract/tables/table_j.c\n",
      "ta-lib/src/ta_abstract/tables/table_e.c\n",
      "ta-lib/src/ta_abstract/tables/table_t.c\n",
      "ta-lib/src/ta_abstract/tables/table_n.c\n",
      "ta-lib/src/ta_abstract/tables/table_i.c\n",
      "ta-lib/src/ta_abstract/tables/table_c.c\n",
      "ta-lib/src/ta_abstract/tables/table_l.c\n",
      "ta-lib/src/ta_abstract/tables/table_k.c\n",
      "ta-lib/src/ta_abstract/tables/table_g.c\n",
      "ta-lib/src/ta_abstract/tables/table_d.c\n",
      "ta-lib/src/ta_abstract/tables/table_h.c\n",
      "ta-lib/src/ta_abstract/tables/table_o.c\n",
      "ta-lib/src/ta_abstract/tables/table_b.c\n",
      "ta-lib/src/ta_abstract/tables/table_q.c\n",
      "ta-lib/src/ta_abstract/tables/table_v.c\n",
      "ta-lib/src/ta_abstract/tables/table_m.c\n",
      "ta-lib/src/ta_abstract/tables/table_s.c\n",
      "ta-lib/src/ta_abstract/tables/table_y.c\n",
      "ta-lib/src/ta_abstract/tables/table_p.c\n",
      "ta-lib/src/ta_abstract/tables/table_z.c\n",
      "ta-lib/src/ta_abstract/tables/table_a.c\n",
      "ta-lib/src/ta_abstract/tables/table_w.c\n",
      "ta-lib/src/ta_abstract/ta_def_ui.c\n",
      "ta-lib/src/ta_abstract/templates/\n",
      "ta-lib/src/ta_abstract/templates/ta_x.c.template\n",
      "ta-lib/src/ta_abstract/templates/ta_java_defs.h.template\n",
      "ta-lib/src/ta_abstract/templates/excel_glue.c.template\n",
      "ta-lib/src/ta_abstract/templates/ta_group_idx.c.template\n",
      "ta-lib/src/ta_abstract/templates/ta_frame.c.template\n",
      "ta-lib/src/ta_abstract/templates/CoreAnnotated.java.template\n",
      "ta-lib/src/ta_abstract/templates/ta_func.h.template\n",
      "ta-lib/src/ta_abstract/templates/ta_frame.h.template\n",
      "ta-lib/src/ta_abstract/templates/Makefile.am.template\n",
      "ta-lib/src/ta_abstract/templates/ta_func_api.c.template\n",
      "ta-lib/src/ta_abstract/templates/ta_func.swg.template\n",
      "ta-lib/src/ta_abstract/templates/ta_retcode.c.template\n",
      "ta-lib/src/ta_abstract/ta_java_defs.h\n",
      "ta-lib/src/Makefile.am\n",
      "ta-lib/src/tools/\n",
      "ta-lib/src/tools/ta_regtest/\n",
      "ta-lib/src/tools/ta_regtest/test_util.c\n",
      "ta-lib/src/tools/ta_regtest/ta_test_func.h\n",
      "ta-lib/src/tools/ta_regtest/test_data.c\n",
      "ta-lib/src/tools/ta_regtest/ta_gDataHigh.c\n",
      "ta-lib/src/tools/ta_regtest/Makefile.in\n",
      "ta-lib/src/tools/ta_regtest/test_internals.c\n",
      "ta-lib/src/tools/ta_regtest/Makefile.am\n",
      "ta-lib/src/tools/ta_regtest/ta_regtest.c\n",
      "ta-lib/src/tools/ta_regtest/ta_gDataOpen.c\n",
      "ta-lib/src/tools/ta_regtest/ta_gDataClose.c\n",
      "ta-lib/src/tools/ta_regtest/test_abstract.c\n",
      "ta-lib/src/tools/ta_regtest/ta_test_func/\n",
      "ta-lib/src/tools/ta_regtest/ta_test_func/test_bbands.c\n",
      "ta-lib/src/tools/ta_regtest/ta_test_func/test_stddev.c\n",
      "ta-lib/src/tools/ta_regtest/ta_test_func/test_1in_2out.c\n",
      "ta-lib/src/tools/ta_regtest/ta_test_func/test_sar.c\n",
      "ta-lib/src/tools/ta_regtest/ta_test_func/test_1in_1out.c\n",
      "ta-lib/src/tools/ta_regtest/ta_test_func/test_trange.c\n",
      "ta-lib/src/tools/ta_regtest/ta_test_func/test_macd.c\n",
      "ta-lib/src/tools/ta_regtest/ta_test_func/test_po.c\n",
      "ta-lib/src/tools/ta_regtest/ta_test_func/test_per_hlc.c\n",
      "ta-lib/src/tools/ta_regtest/ta_test_func/test_mom.c\n",
      "ta-lib/src/tools/ta_regtest/ta_test_func/test_per_ohlc.c\n",
      "ta-lib/src/tools/ta_regtest/ta_test_func/test_adx.c\n",
      "ta-lib/src/tools/ta_regtest/ta_test_func/test_candlestick.c\n",
      "ta-lib/src/tools/ta_regtest/ta_test_func/test_rsi.c\n",
      "ta-lib/src/tools/ta_regtest/ta_test_func/test_per_ema.c\n",
      "ta-lib/src/tools/ta_regtest/ta_test_func/test_minmax.c\n",
      "ta-lib/src/tools/ta_regtest/ta_test_func/test_per_hlcv.c\n",
      "ta-lib/src/tools/ta_regtest/ta_test_func/test_per_hl.c\n",
      "ta-lib/src/tools/ta_regtest/ta_test_func/test_stoch.c\n",
      "ta-lib/src/tools/ta_regtest/ta_test_func/test_ma.c\n",
      "ta-lib/src/tools/ta_regtest/ta_error_number.h\n",
      "ta-lib/src/tools/ta_regtest/ta_test_priv.h\n",
      "ta-lib/src/tools/ta_regtest/ReadMe.txt\n",
      "ta-lib/src/tools/ta_regtest/ta_gDataLow.c\n",
      "ta-lib/src/tools/Makefile.in\n",
      "ta-lib/src/tools/Makefile.am\n",
      "ta-lib/src/tools/gen_code/\n",
      "ta-lib/src/tools/gen_code/java/\n",
      "ta-lib/src/tools/gen_code/java/PrettyCode.java\n",
      "ta-lib/src/tools/gen_code/java/Main.java\n",
      "ta-lib/src/tools/gen_code/gen_code.c\n",
      "ta-lib/src/tools/gen_code/Makefile.in\n",
      "ta-lib/src/tools/gen_code/Makefile.am\n",
      "ta-lib/src/tools/gen_code/mcpp.exe\n",
      "checking for a BSD-compatible install... /usr/bin/install -c\n",
      "checking whether build environment is sane... yes\n",
      "checking for a thread-safe mkdir -p... /bin/mkdir -p\n",
      "checking for gawk... no\n",
      "checking for mawk... mawk\n",
      "checking whether make sets $(MAKE)... yes\n",
      "checking for gcc... gcc\n",
      "checking for C compiler default output file name... a.out\n",
      "checking whether the C compiler works... yes\n",
      "checking whether we are cross compiling... no\n",
      "checking for suffix of executables... \n",
      "checking for suffix of object files... o\n",
      "checking whether we are using the GNU C compiler... yes\n",
      "checking whether gcc accepts -g... yes\n",
      "checking for gcc option to accept ISO C89... none needed\n",
      "checking for style of include used by make... GNU\n",
      "checking dependency style of gcc... gcc3\n",
      "checking build system type... x86_64-unknown-linux-gnu\n",
      "checking host system type... x86_64-unknown-linux-gnu\n",
      "checking for a sed that does not truncate output... /bin/sed\n",
      "checking for grep that handles long lines and -e... /bin/grep\n",
      "checking for egrep... /bin/grep -E\n",
      "checking for ld used by gcc... /usr/bin/ld\n",
      "checking if the linker (/usr/bin/ld) is GNU ld... yes\n",
      "checking for /usr/bin/ld option to reload object files... -r\n",
      "checking for BSD-compatible nm... /usr/bin/nm -B\n",
      "checking whether ln -s works... yes\n",
      "checking how to recognise dependent libraries... pass_all\n",
      "./configure: line 4354: /usr/bin/file: No such file or directory\n",
      "checking how to run the C preprocessor... gcc -E\n",
      "checking for ANSI C header files... yes\n",
      "checking for sys/types.h... yes\n",
      "checking for sys/stat.h... yes\n",
      "checking for stdlib.h... yes\n",
      "checking for string.h... yes\n",
      "checking for memory.h... yes\n",
      "checking for strings.h... yes\n",
      "checking for inttypes.h... yes\n",
      "checking for stdint.h... yes\n",
      "checking for unistd.h... yes\n",
      "checking dlfcn.h usability... yes\n",
      "checking dlfcn.h presence... yes\n",
      "checking for dlfcn.h... yes\n",
      "checking for g++... g++\n",
      "checking whether we are using the GNU C++ compiler... yes\n",
      "checking whether g++ accepts -g... yes\n",
      "checking dependency style of g++... gcc3\n",
      "checking how to run the C++ preprocessor... g++ -E\n",
      "checking for g77... no\n",
      "checking for xlf... no\n",
      "checking for f77... f77\n",
      "checking whether we are using the GNU Fortran 77 compiler... yes\n",
      "checking whether f77 accepts -g... yes\n",
      "checking the maximum length of command line arguments... 32768\n",
      "checking command to parse /usr/bin/nm -B output from gcc object... ok\n",
      "checking for objdir... .libs\n",
      "checking for ar... ar\n",
      "checking for ranlib... ranlib\n",
      "checking for strip... strip\n",
      "checking if gcc supports -fno-rtti -fno-exceptions... no\n",
      "checking for gcc option to produce PIC... -fPIC\n",
      "checking if gcc PIC flag -fPIC works... yes\n",
      "checking if gcc static flag -static works... yes\n",
      "checking if gcc supports -c -o file.o... yes\n",
      "checking whether the gcc linker (/usr/bin/ld) supports shared libraries... yes\n",
      "checking whether -lc should be explicitly linked in... no\n",
      "checking dynamic linker characteristics... GNU/Linux ld.so\n",
      "checking how to hardcode library paths into programs... immediate\n",
      "checking whether stripping libraries is possible... yes\n",
      "checking if libtool supports shared libraries... yes\n",
      "checking whether to build shared libraries... yes\n",
      "checking whether to build static libraries... yes\n",
      "configure: creating libtool\n",
      "appending configuration tag \"CXX\" to libtool\n",
      "checking for ld used by g++... /usr/bin/ld\n",
      "checking if the linker (/usr/bin/ld) is GNU ld... yes\n",
      "checking whether the g++ linker (/usr/bin/ld) supports shared libraries... yes\n",
      "checking for g++ option to produce PIC... -fPIC\n",
      "checking if g++ PIC flag -fPIC works... yes\n",
      "checking if g++ static flag -static works... yes\n",
      "checking if g++ supports -c -o file.o... yes\n",
      "checking whether the g++ linker (/usr/bin/ld) supports shared libraries... yes\n",
      "checking dynamic linker characteristics... GNU/Linux ld.so\n",
      "checking how to hardcode library paths into programs... immediate\n",
      "appending configuration tag \"F77\" to libtool\n",
      "checking if libtool supports shared libraries... yes\n",
      "checking whether to build shared libraries... yes\n",
      "checking whether to build static libraries... yes\n",
      "checking for f77 option to produce PIC... -fPIC\n",
      "checking if f77 PIC flag -fPIC works... yes\n",
      "checking if f77 static flag -static works... yes\n",
      "checking if f77 supports -c -o file.o... yes\n",
      "checking whether the f77 linker (/usr/bin/ld) supports shared libraries... yes\n",
      "checking dynamic linker characteristics... GNU/Linux ld.so\n",
      "checking how to hardcode library paths into programs... immediate\n",
      "checking for dlopen in -ldl... yes\n",
      "checking for pthread_create in -lpthread... yes\n",
      "checking for ANSI C header files... (cached) yes\n",
      "checking float.h usability... yes\n",
      "checking float.h presence... yes\n",
      "checking for float.h... yes\n",
      "checking for inttypes.h... (cached) yes\n",
      "checking limits.h usability... yes\n",
      "checking limits.h presence... yes\n",
      "checking for limits.h... yes\n",
      "checking locale.h usability... yes\n",
      "checking locale.h presence... yes\n",
      "checking for locale.h... yes\n",
      "checking stddef.h usability... yes\n",
      "checking stddef.h presence... yes\n",
      "checking for stddef.h... yes\n",
      "checking for stdint.h... (cached) yes\n",
      "checking for stdlib.h... (cached) yes\n",
      "checking for string.h... (cached) yes\n",
      "checking for unistd.h... (cached) yes\n",
      "checking wchar.h usability... yes\n",
      "checking wchar.h presence... yes\n",
      "checking for wchar.h... yes\n",
      "checking wctype.h usability... yes\n",
      "checking wctype.h presence... yes\n",
      "checking for wctype.h... yes\n",
      "checking for an ANSI C-conforming const... yes\n",
      "checking for size_t... yes\n",
      "checking whether struct tm is in sys/time.h or time.h... time.h\n",
      "checking for working volatile... yes\n",
      "checking for ptrdiff_t... yes\n",
      "checking return type of signal handlers... void\n",
      "checking for working strcoll... yes\n",
      "checking for strftime... yes\n",
      "checking for working strtod... yes\n",
      "checking for vprintf... yes\n",
      "checking for _doprnt... no\n",
      "checking for floor... no\n",
      "checking for isascii... yes\n",
      "checking for localeconv... yes\n",
      "checking for mblen... yes\n",
      "checking for memmove... yes\n",
      "checking for memset... yes\n",
      "checking for modf... yes\n",
      "checking for pow... no\n",
      "checking for sqrt... no\n",
      "checking for strcasecmp... yes\n",
      "checking for strchr... yes\n",
      "checking for strerror... yes\n",
      "checking for strncasecmp... yes\n",
      "checking for strrchr... yes\n",
      "checking for strstr... yes\n",
      "checking for strtol... yes\n",
      "checking for strtoul... yes\n",
      "configure: creating ./config.status\n",
      "config.status: creating Makefile\n",
      "config.status: creating src/Makefile\n",
      "config.status: creating src/ta_abstract/Makefile\n",
      "config.status: creating src/ta_common/Makefile\n",
      "config.status: creating src/ta_func/Makefile\n",
      "config.status: creating src/tools/Makefile\n",
      "config.status: creating src/tools/gen_code/Makefile\n",
      "config.status: creating src/tools/ta_regtest/Makefile\n",
      "config.status: creating ta-lib-config\n",
      "config.status: creating ta-lib.spec\n",
      "config.status: creating ta-lib.dpkg\n",
      "config.status: creating include/ta_config.h\n",
      "config.status: include/ta_config.h is unchanged\n",
      "config.status: executing depfiles commands\n",
      "Making all in src\n",
      "make[1]: Entering directory '/FinRL-Meta/ta-lib/src'\n",
      "Making all in ta_abstract\n",
      "make[2]: Entering directory '/FinRL-Meta/ta-lib/src/ta_abstract'\n",
      "make[2]: Nothing to be done for 'all'.\n",
      "make[2]: Leaving directory '/FinRL-Meta/ta-lib/src/ta_abstract'\n",
      "Making all in ta_common\n",
      "make[2]: Entering directory '/FinRL-Meta/ta-lib/src/ta_common'\n",
      "make[2]: Nothing to be done for 'all'.\n",
      "make[2]: Leaving directory '/FinRL-Meta/ta-lib/src/ta_common'\n",
      "Making all in ta_func\n",
      "make[2]: Entering directory '/FinRL-Meta/ta-lib/src/ta_func'\n",
      "make[2]: Nothing to be done for 'all'.\n",
      "make[2]: Leaving directory '/FinRL-Meta/ta-lib/src/ta_func'\n",
      "make[2]: Entering directory '/FinRL-Meta/ta-lib/src'\n",
      "make[2]: Nothing to be done for 'all-am'.\n",
      "make[2]: Leaving directory '/FinRL-Meta/ta-lib/src'\n",
      "make[1]: Leaving directory '/FinRL-Meta/ta-lib/src'\n",
      "Making all in src/tools\n",
      "make[1]: Entering directory '/FinRL-Meta/ta-lib/src/tools'\n",
      "Making all in gen_code\n",
      "make[2]: Entering directory '/FinRL-Meta/ta-lib/src/tools/gen_code'\n",
      "make  gen_code\n",
      "make[3]: Entering directory '/FinRL-Meta/ta-lib/src/tools/gen_code'\n",
      "make[3]: 'gen_code' is up to date.\n",
      "make[3]: Leaving directory '/FinRL-Meta/ta-lib/src/tools/gen_code'\n",
      "cp gen_code ../../../bin\n",
      "make[2]: Leaving directory '/FinRL-Meta/ta-lib/src/tools/gen_code'\n",
      "Making all in ta_regtest\n",
      "make[2]: Entering directory '/FinRL-Meta/ta-lib/src/tools/ta_regtest'\n",
      "make[2]: Nothing to be done for 'all'.\n",
      "make[2]: Leaving directory '/FinRL-Meta/ta-lib/src/tools/ta_regtest'\n",
      "make[2]: Entering directory '/FinRL-Meta/ta-lib/src/tools'\n",
      "make[2]: Nothing to be done for 'all-am'.\n",
      "make[2]: Leaving directory '/FinRL-Meta/ta-lib/src/tools'\n",
      "make[1]: Leaving directory '/FinRL-Meta/ta-lib/src/tools'\n",
      "make[1]: Entering directory '/FinRL-Meta/ta-lib'\n",
      "make[1]: Nothing to be done for 'all-am'.\n",
      "make[1]: Leaving directory '/FinRL-Meta/ta-lib'\n",
      "Making install in src\n",
      "make[1]: Entering directory '/FinRL-Meta/ta-lib/src'\n",
      "Making install in ta_abstract\n",
      "make[2]: Entering directory '/FinRL-Meta/ta-lib/src/ta_abstract'\n",
      "make[3]: Entering directory '/FinRL-Meta/ta-lib/src/ta_abstract'\n",
      "make[3]: Nothing to be done for 'install-exec-am'.\n",
      "test -z \"/usr/include/ta-lib/\" || /bin/mkdir -p \"/usr/include/ta-lib/\"\n",
      " /usr/bin/install -c -m 644 '../../include/ta_defs.h' '/usr/include/ta-lib//ta_defs.h'\n",
      " /usr/bin/install -c -m 644 '../../include/ta_libc.h' '/usr/include/ta-lib//ta_libc.h'\n",
      " /usr/bin/install -c -m 644 '../../include/ta_abstract.h' '/usr/include/ta-lib//ta_abstract.h'\n",
      "make[3]: Leaving directory '/FinRL-Meta/ta-lib/src/ta_abstract'\n",
      "make[2]: Leaving directory '/FinRL-Meta/ta-lib/src/ta_abstract'\n",
      "Making install in ta_common\n",
      "make[2]: Entering directory '/FinRL-Meta/ta-lib/src/ta_common'\n",
      "make[3]: Entering directory '/FinRL-Meta/ta-lib/src/ta_common'\n",
      "make[3]: Nothing to be done for 'install-exec-am'.\n",
      "test -z \"/usr/include/ta-lib/\" || /bin/mkdir -p \"/usr/include/ta-lib/\"\n",
      " /usr/bin/install -c -m 644 '../../include/ta_defs.h' '/usr/include/ta-lib//ta_defs.h'\n",
      " /usr/bin/install -c -m 644 '../../include/ta_libc.h' '/usr/include/ta-lib//ta_libc.h'\n",
      " /usr/bin/install -c -m 644 '../../include/ta_common.h' '/usr/include/ta-lib//ta_common.h'\n",
      "make[3]: Leaving directory '/FinRL-Meta/ta-lib/src/ta_common'\n",
      "make[2]: Leaving directory '/FinRL-Meta/ta-lib/src/ta_common'\n",
      "Making install in ta_func\n",
      "make[2]: Entering directory '/FinRL-Meta/ta-lib/src/ta_func'\n",
      "make[3]: Entering directory '/FinRL-Meta/ta-lib/src/ta_func'\n",
      "make[3]: Nothing to be done for 'install-exec-am'.\n",
      "test -z \"/usr/include/ta-lib/\" || /bin/mkdir -p \"/usr/include/ta-lib/\"\n",
      " /usr/bin/install -c -m 644 '../../include/ta_defs.h' '/usr/include/ta-lib//ta_defs.h'\n",
      " /usr/bin/install -c -m 644 '../../include/ta_libc.h' '/usr/include/ta-lib//ta_libc.h'\n",
      " /usr/bin/install -c -m 644 '../../include/ta_func.h' '/usr/include/ta-lib//ta_func.h'\n",
      "make[3]: Leaving directory '/FinRL-Meta/ta-lib/src/ta_func'\n",
      "make[2]: Leaving directory '/FinRL-Meta/ta-lib/src/ta_func'\n",
      "make[2]: Entering directory '/FinRL-Meta/ta-lib/src'\n",
      "make[3]: Entering directory '/FinRL-Meta/ta-lib/src'\n",
      "test -z \"/usr/lib\" || /bin/mkdir -p \"/usr/lib\"\n",
      " /bin/bash ../libtool --mode=install /usr/bin/install -c  'libta_lib.la' '/usr/lib/libta_lib.la'\n",
      "/usr/bin/install -c .libs/libta_lib.so.0.0.0 /usr/lib/libta_lib.so.0.0.0\n",
      "(cd /usr/lib && { ln -s -f libta_lib.so.0.0.0 libta_lib.so.0 || { rm -f libta_lib.so.0 && ln -s libta_lib.so.0.0.0 libta_lib.so.0; }; })\n",
      "(cd /usr/lib && { ln -s -f libta_lib.so.0.0.0 libta_lib.so || { rm -f libta_lib.so && ln -s libta_lib.so.0.0.0 libta_lib.so; }; })\n",
      "/usr/bin/install -c .libs/libta_lib.lai /usr/lib/libta_lib.la\n",
      "/usr/bin/install -c .libs/libta_lib.a /usr/lib/libta_lib.a\n",
      "chmod 644 /usr/lib/libta_lib.a\n",
      "ranlib /usr/lib/libta_lib.a\n",
      "PATH=\"$PATH:/sbin\" ldconfig -n /usr/lib\n",
      "----------------------------------------------------------------------\n",
      "Libraries have been installed in:\n",
      "   /usr/lib\n",
      "\n",
      "If you ever happen to want to link against installed libraries\n",
      "in a given directory, LIBDIR, you must either use libtool, and\n",
      "specify the full pathname of the library, or use the `-LLIBDIR'\n",
      "flag during linking and do at least one of the following:\n",
      "   - add LIBDIR to the `LD_LIBRARY_PATH' environment variable\n",
      "     during execution\n",
      "   - add LIBDIR to the `LD_RUN_PATH' environment variable\n",
      "     during linking\n",
      "   - use the `-Wl,--rpath -Wl,LIBDIR' linker flag\n",
      "   - have your system administrator add LIBDIR to `/etc/ld.so.conf'\n",
      "\n",
      "See any operating system documentation about shared libraries for\n",
      "more information, such as the ld(1) and ld.so(8) manual pages.\n",
      "----------------------------------------------------------------------\n",
      "make[3]: Nothing to be done for 'install-data-am'.\n",
      "make[3]: Leaving directory '/FinRL-Meta/ta-lib/src'\n",
      "make[2]: Leaving directory '/FinRL-Meta/ta-lib/src'\n",
      "make[1]: Leaving directory '/FinRL-Meta/ta-lib/src'\n",
      "Making install in src/tools\n",
      "make[1]: Entering directory '/FinRL-Meta/ta-lib/src/tools'\n",
      "Making install in gen_code\n",
      "make[2]: Entering directory '/FinRL-Meta/ta-lib/src/tools/gen_code'\n",
      "make  gen_code\n",
      "make[3]: Entering directory '/FinRL-Meta/ta-lib/src/tools/gen_code'\n",
      "make[3]: 'gen_code' is up to date.\n",
      "make[3]: Leaving directory '/FinRL-Meta/ta-lib/src/tools/gen_code'\n",
      "cp gen_code ../../../bin\n",
      "make[3]: Entering directory '/FinRL-Meta/ta-lib/src/tools/gen_code'\n",
      "make[3]: Nothing to be done for 'install-exec-am'.\n",
      "make[3]: Nothing to be done for 'install-data-am'.\n",
      "make[3]: Leaving directory '/FinRL-Meta/ta-lib/src/tools/gen_code'\n",
      "make[2]: Leaving directory '/FinRL-Meta/ta-lib/src/tools/gen_code'\n",
      "Making install in ta_regtest\n",
      "make[2]: Entering directory '/FinRL-Meta/ta-lib/src/tools/ta_regtest'\n",
      "make[3]: Entering directory '/FinRL-Meta/ta-lib/src/tools/ta_regtest'\n",
      "make[3]: Nothing to be done for 'install-exec-am'.\n",
      "make[3]: Nothing to be done for 'install-data-am'.\n",
      "make[3]: Leaving directory '/FinRL-Meta/ta-lib/src/tools/ta_regtest'\n",
      "make[2]: Leaving directory '/FinRL-Meta/ta-lib/src/tools/ta_regtest'\n",
      "make[2]: Entering directory '/FinRL-Meta/ta-lib/src/tools'\n",
      "make[3]: Entering directory '/FinRL-Meta/ta-lib/src/tools'\n",
      "make[3]: Nothing to be done for 'install-exec-am'.\n",
      "make[3]: Nothing to be done for 'install-data-am'.\n",
      "make[3]: Leaving directory '/FinRL-Meta/ta-lib/src/tools'\n",
      "make[2]: Leaving directory '/FinRL-Meta/ta-lib/src/tools'\n",
      "make[1]: Leaving directory '/FinRL-Meta/ta-lib/src/tools'\n",
      "make[1]: Entering directory '/FinRL-Meta/ta-lib'\n",
      "make[2]: Entering directory '/FinRL-Meta/ta-lib'\n",
      "test -z \"/usr/bin\" || /bin/mkdir -p \"/usr/bin\"\n",
      " /usr/bin/install -c 'ta-lib-config' '/usr/bin/ta-lib-config'\n",
      "make[2]: Nothing to be done for 'install-data-am'.\n",
      "make[2]: Leaving directory '/FinRL-Meta/ta-lib'\n",
      "make[1]: Leaving directory '/FinRL-Meta/ta-lib'\n",
      "Requirement already satisfied: TA-Lib in /usr/local/lib/python3.7/dist-packages (0.4.24)\n",
      "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from TA-Lib) (1.21.6)\n"
     ]
    }
   ],
   "source": [
    "#install TA-lib (technical analysis)\n",
    "!wget http://prdownloads.sourceforge.net/ta-lib/ta-lib-0.4.0-src.tar.gz \n",
    "!tar xvzf ta-lib-0.4.0-src.tar.gz\n",
    "import os\n",
    "os.chdir('ta-lib') \n",
    "!./configure --prefix=/usr\n",
    "!make\n",
    "!make install\n",
    "os.chdir('../')\n",
    "!cd\n",
    "!pip install TA-Lib\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "jbql-67vW_cv"
   },
   "outputs": [],
   "source": [
    "# Other imports\n",
    "\n",
    "import scipy as sp\n",
    "import math\n",
    "import pandas as pd\n",
    "import requests\n",
    "import json\n",
    "import matplotlib.dates as mdates\n",
    "import numpy as np\n",
    "import pickle\n",
    "import shutil\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from meta.data_processor import DataProcessor\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "import seaborn as sns\n",
    "\n",
    "from datetime import datetime, timedelta\n",
    "from talib.abstract import MACD, RSI, CCI, DX\n",
    "from binance.client import Client\n",
    "from pandas.testing import assert_frame_equal\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler \n",
    "from torch.utils.data import Dataset, DataLoader, WeightedRandomSampler\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "#from google.colab import files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "YGYjgA5KVciR"
   },
   "outputs": [],
   "source": [
    "# Plot settings\n",
    "\n",
    "SCALE_FACTOR = 2\n",
    "\n",
    "plt.style.use('seaborn')\n",
    "plt.rcParams['figure.figsize'] = [5 * SCALE_FACTOR, 2 * SCALE_FACTOR]\n",
    "plt.rcParams['figure.dpi'] = 300 * SCALE_FACTOR\n",
    "plt.rcParams['font.size'] = 5 * SCALE_FACTOR\n",
    "plt.rcParams['axes.labelsize'] = 5 * SCALE_FACTOR\n",
    "plt.rcParams['axes.titlesize'] = 6 * SCALE_FACTOR\n",
    "plt.rcParams['xtick.labelsize'] = 4 * SCALE_FACTOR\n",
    "plt.rcParams['ytick.labelsize'] = 4 * SCALE_FACTOR\n",
    "plt.rcParams['font.family'] = 'serif'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "b4EJ9VW3mvQl"
   },
   "source": [
    "# Part 1: Adapted Binance downloader \n",
    "\n",
    "\n",
    "Any features you think of are probably coming out of OHLCV data or alternative data streams. The functions required to obtain the new features are added here and added to the eventual dataframe."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Bn6lsnoimkX5"
   },
   "source": [
    "## 1.1 Set contants and use BinanceProcessor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "S2rOJoF5nDY5"
   },
   "outputs": [],
   "source": [
    "# Set constants:\n",
    "\n",
    "ticker_list = ['ETHUSDT']\n",
    "\n",
    "\n",
    "TIME_INTERVAL = '1h'\n",
    "\n",
    "TRAIN_START_DATE = '2015-01-01'\n",
    "TRAIN_END_DATE= '2019-08-01'\n",
    "TRADE_START_DATE = '2019-08-01'\n",
    "TRADE_END_DATE = '2020-01-03'\n",
    "\n",
    "\n",
    "technical_indicator_list = ['macd',\n",
    "                             'macd_signal',\n",
    "                             'macd_hist',\n",
    "                             'rsi',\n",
    "                             'cci',\n",
    "                             'dx'\n",
    "                             ]\n",
    "\n",
    "if_vix = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kWvm7WQ0nCBr",
    "outputId": "39e3cd74-afe1-4e09-d19b-aaa21d7ab5e0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "binance successfully connected\n"
     ]
    }
   ],
   "source": [
    "# Process data using unified data processor\n",
    "p = DataProcessor(data_source='binance', start_date=TRAIN_START_DATE, end_date=TRADE_END_DATE, time_interval=TIME_INTERVAL)\n",
    "p.download_data(ticker_list=ticker_list)\n",
    "p.clean_data()\n",
    "df = p.dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "OWfalHj87aEp",
    "outputId": "1bbc1861-0f4d-4a47-b2d4-480135a01a74"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-2f8ca6ce-65fc-451c-99b1-c68973e8e17e\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tic</th>\n",
       "      <th>time</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adjusted_close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>2017-08-17 04:00:00</td>\n",
       "      <td>301.13</td>\n",
       "      <td>302.57</td>\n",
       "      <td>298.0</td>\n",
       "      <td>301.61</td>\n",
       "      <td>301.61</td>\n",
       "      <td>125.66877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>2017-08-17 05:00:00</td>\n",
       "      <td>301.61</td>\n",
       "      <td>303.28</td>\n",
       "      <td>300.0</td>\n",
       "      <td>303.10</td>\n",
       "      <td>303.10</td>\n",
       "      <td>377.67246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>2017-08-17 06:00:00</td>\n",
       "      <td>302.40</td>\n",
       "      <td>304.44</td>\n",
       "      <td>301.9</td>\n",
       "      <td>302.68</td>\n",
       "      <td>302.68</td>\n",
       "      <td>303.86672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>2017-08-17 07:00:00</td>\n",
       "      <td>302.68</td>\n",
       "      <td>307.96</td>\n",
       "      <td>302.6</td>\n",
       "      <td>307.96</td>\n",
       "      <td>307.96</td>\n",
       "      <td>754.74510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>2017-08-17 08:00:00</td>\n",
       "      <td>307.95</td>\n",
       "      <td>309.97</td>\n",
       "      <td>307.0</td>\n",
       "      <td>308.62</td>\n",
       "      <td>308.62</td>\n",
       "      <td>150.75029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2f8ca6ce-65fc-451c-99b1-c68973e8e17e')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-2f8ca6ce-65fc-451c-99b1-c68973e8e17e button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-2f8ca6ce-65fc-451c-99b1-c68973e8e17e');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "       tic                 time    open    high    low   close  \\\n",
       "0  ETHUSDT  2017-08-17 04:00:00  301.13  302.57  298.0  301.61   \n",
       "1  ETHUSDT  2017-08-17 05:00:00  301.61  303.28  300.0  303.10   \n",
       "2  ETHUSDT  2017-08-17 06:00:00  302.40  304.44  301.9  302.68   \n",
       "3  ETHUSDT  2017-08-17 07:00:00  302.68  307.96  302.6  307.96   \n",
       "4  ETHUSDT  2017-08-17 08:00:00  307.95  309.97  307.0  308.62   \n",
       "\n",
       "   adjusted_close     volume  \n",
       "0          301.61  125.66877  \n",
       "1          303.10  377.67246  \n",
       "2          302.68  303.86672  \n",
       "3          307.96  754.74510  \n",
       "4          308.62  150.75029  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ChdO8tqhvg93"
   },
   "source": [
    "## 1.2 Add technical indicators\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "vc3FUtJfRvXA"
   },
   "outputs": [],
   "source": [
    "def add_technical_indicator(df, tech_indicator_list):\n",
    "    # print('Adding self-defined technical indicators is NOT supported yet.')\n",
    "    # print('Use default: MACD, RSI, CCI, DX.')\n",
    "\n",
    "    final_df = pd.DataFrame()\n",
    "    for i in df.tic.unique():\n",
    "        tic_df = df[df.tic == i].copy()\n",
    "        tic_df['rsi'] = RSI(tic_df['close'], timeperiod=14)\n",
    "        tic_df['macd'], tic_df['macd_signal'], tic_df['macd_hist'] = MACD(tic_df['close'], fastperiod=12,\n",
    "                                                                          slowperiod=26, signalperiod=9)\n",
    "        tic_df['cci'] = CCI(tic_df['high'], tic_df['low'], tic_df['close'], timeperiod=14)\n",
    "        tic_df['dx'] = DX(tic_df['high'], tic_df['low'], tic_df['close'], timeperiod=14)\n",
    "        final_df = final_df.append(tic_df)\n",
    "    return final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 337
    },
    "id": "nkI0v73_RvyR",
    "outputId": "19d2d266-e15f-4eef-8d25-f29217696f28"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-33a377be-9a73-47a4-a1d5-5facc419c8ab\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tic</th>\n",
       "      <th>time</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adjusted_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>rsi</th>\n",
       "      <th>macd</th>\n",
       "      <th>macd_signal</th>\n",
       "      <th>macd_hist</th>\n",
       "      <th>cci</th>\n",
       "      <th>dx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>2020-01-02 19:00:00</td>\n",
       "      <td>127.49</td>\n",
       "      <td>127.67</td>\n",
       "      <td>127.08</td>\n",
       "      <td>127.62</td>\n",
       "      <td>127.62</td>\n",
       "      <td>4747.08642</td>\n",
       "      <td>32.909162</td>\n",
       "      <td>-0.756788</td>\n",
       "      <td>-0.534229</td>\n",
       "      <td>-0.222559</td>\n",
       "      <td>-123.397792</td>\n",
       "      <td>59.185403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>2020-01-02 20:00:00</td>\n",
       "      <td>127.63</td>\n",
       "      <td>127.64</td>\n",
       "      <td>126.79</td>\n",
       "      <td>127.16</td>\n",
       "      <td>127.16</td>\n",
       "      <td>5796.42079</td>\n",
       "      <td>30.316621</td>\n",
       "      <td>-0.820850</td>\n",
       "      <td>-0.591553</td>\n",
       "      <td>-0.229297</td>\n",
       "      <td>-111.749560</td>\n",
       "      <td>61.459160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>2020-01-02 21:00:00</td>\n",
       "      <td>127.16</td>\n",
       "      <td>127.87</td>\n",
       "      <td>127.15</td>\n",
       "      <td>127.72</td>\n",
       "      <td>127.72</td>\n",
       "      <td>2225.95265</td>\n",
       "      <td>36.839907</td>\n",
       "      <td>-0.817015</td>\n",
       "      <td>-0.636646</td>\n",
       "      <td>-0.180369</td>\n",
       "      <td>-72.347677</td>\n",
       "      <td>54.125554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>2020-01-02 22:00:00</td>\n",
       "      <td>127.72</td>\n",
       "      <td>127.76</td>\n",
       "      <td>127.24</td>\n",
       "      <td>127.39</td>\n",
       "      <td>127.39</td>\n",
       "      <td>2654.51024</td>\n",
       "      <td>34.774037</td>\n",
       "      <td>-0.831024</td>\n",
       "      <td>-0.675521</td>\n",
       "      <td>-0.155502</td>\n",
       "      <td>-68.144606</td>\n",
       "      <td>54.125554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>2020-01-02 23:00:00</td>\n",
       "      <td>127.39</td>\n",
       "      <td>127.48</td>\n",
       "      <td>126.91</td>\n",
       "      <td>127.19</td>\n",
       "      <td>127.19</td>\n",
       "      <td>2980.68108</td>\n",
       "      <td>33.546234</td>\n",
       "      <td>-0.848483</td>\n",
       "      <td>-0.710114</td>\n",
       "      <td>-0.138370</td>\n",
       "      <td>-74.355556</td>\n",
       "      <td>57.349132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-33a377be-9a73-47a4-a1d5-5facc419c8ab')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-33a377be-9a73-47a4-a1d5-5facc419c8ab button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-33a377be-9a73-47a4-a1d5-5facc419c8ab');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "         tic                 time    open    high     low   close  \\\n",
       "296  ETHUSDT  2020-01-02 19:00:00  127.49  127.67  127.08  127.62   \n",
       "297  ETHUSDT  2020-01-02 20:00:00  127.63  127.64  126.79  127.16   \n",
       "298  ETHUSDT  2020-01-02 21:00:00  127.16  127.87  127.15  127.72   \n",
       "299  ETHUSDT  2020-01-02 22:00:00  127.72  127.76  127.24  127.39   \n",
       "300  ETHUSDT  2020-01-02 23:00:00  127.39  127.48  126.91  127.19   \n",
       "\n",
       "     adjusted_close      volume        rsi      macd  macd_signal  macd_hist  \\\n",
       "296          127.62  4747.08642  32.909162 -0.756788    -0.534229  -0.222559   \n",
       "297          127.16  5796.42079  30.316621 -0.820850    -0.591553  -0.229297   \n",
       "298          127.72  2225.95265  36.839907 -0.817015    -0.636646  -0.180369   \n",
       "299          127.39  2654.51024  34.774037 -0.831024    -0.675521  -0.155502   \n",
       "300          127.19  2980.68108  33.546234 -0.848483    -0.710114  -0.138370   \n",
       "\n",
       "            cci         dx  \n",
       "296 -123.397792  59.185403  \n",
       "297 -111.749560  61.459160  \n",
       "298  -72.347677  54.125554  \n",
       "299  -68.144606  54.125554  \n",
       "300  -74.355556  57.349132  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed_df=add_technical_indicator(df,technical_indicator_list)\n",
    "processed_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ofidEftSR4BC",
    "outputId": "9a7df539-ea90-47e5-a1d5-04dcc464a6ab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                         tic    open    high     low   close  adjusted_close  \\\n",
      "time                                                                           \n",
      "2020-01-02 04:00:00  ETHUSDT  129.09  129.87  128.69  129.55          129.55   \n",
      "2020-01-02 05:00:00  ETHUSDT  129.57  129.75  129.30  129.63          129.63   \n",
      "2020-01-02 06:00:00  ETHUSDT  129.60  129.99  129.15  129.42          129.42   \n",
      "2020-01-02 07:00:00  ETHUSDT  129.43  129.67  128.96  129.26          129.26   \n",
      "2020-01-02 08:00:00  ETHUSDT  129.23  130.27  129.21  130.21          130.21   \n",
      "2020-01-02 09:00:00  ETHUSDT  130.21  130.21  129.57  129.98          129.98   \n",
      "2020-01-02 10:00:00  ETHUSDT  129.97  130.28  129.66  129.72          129.72   \n",
      "2020-01-02 11:00:00  ETHUSDT  129.73  129.94  129.44  129.53          129.53   \n",
      "2020-01-02 12:00:00  ETHUSDT  129.52  129.99  129.52  129.96          129.96   \n",
      "2020-01-02 13:00:00  ETHUSDT  129.97  130.01  128.90  129.35          129.35   \n",
      "2020-01-02 14:00:00  ETHUSDT  129.34  129.86  129.21  129.37          129.37   \n",
      "2020-01-02 15:00:00  ETHUSDT  129.37  129.80  129.36  129.59          129.59   \n",
      "2020-01-02 16:00:00  ETHUSDT  129.58  129.78  126.94  127.60          127.60   \n",
      "2020-01-02 17:00:00  ETHUSDT  127.60  127.68  126.38  127.40          127.40   \n",
      "2020-01-02 18:00:00  ETHUSDT  127.42  127.75  127.13  127.49          127.49   \n",
      "2020-01-02 19:00:00  ETHUSDT  127.49  127.67  127.08  127.62          127.62   \n",
      "2020-01-02 20:00:00  ETHUSDT  127.63  127.64  126.79  127.16          127.16   \n",
      "2020-01-02 21:00:00  ETHUSDT  127.16  127.87  127.15  127.72          127.72   \n",
      "2020-01-02 22:00:00  ETHUSDT  127.72  127.76  127.24  127.39          127.39   \n",
      "2020-01-02 23:00:00  ETHUSDT  127.39  127.48  126.91  127.19          127.19   \n",
      "\n",
      "                          volume        rsi      macd  macd_signal  macd_hist  \\\n",
      "time                                                                            \n",
      "2020-01-02 04:00:00   8931.67759  39.615574 -0.290147     0.002150  -0.292297   \n",
      "2020-01-02 05:00:00   7276.74256  40.440563 -0.334118    -0.065103  -0.269015   \n",
      "2020-01-02 06:00:00   4823.43405  38.936740 -0.381514    -0.128385  -0.253128   \n",
      "2020-01-02 07:00:00   6548.36046  37.783893 -0.427063    -0.188121  -0.238942   \n",
      "2020-01-02 08:00:00   7205.67080  47.687738 -0.382099    -0.226916  -0.155182   \n",
      "2020-01-02 09:00:00   5828.65169  45.787379 -0.360864    -0.253706  -0.107158   \n",
      "2020-01-02 10:00:00   8014.34789  43.668866 -0.360855    -0.275136  -0.085719   \n",
      "2020-01-02 11:00:00   4293.24985  42.134640 -0.371893    -0.294487  -0.077406   \n",
      "2020-01-02 12:00:00   3718.46074  46.698734 -0.342000    -0.303990  -0.038011   \n",
      "2020-01-02 13:00:00   8547.00635  41.676744 -0.363344    -0.315861  -0.047483   \n",
      "2020-01-02 14:00:00   5538.92471  41.897368 -0.374330    -0.327554  -0.046775   \n",
      "2020-01-02 15:00:00   5352.62034  44.389350 -0.361121    -0.334268  -0.026854   \n",
      "2020-01-02 16:00:00  32996.89479  31.308701 -0.505404    -0.368495  -0.136909   \n",
      "2020-01-02 17:00:00  40429.91193  30.340999 -0.628641    -0.420524  -0.208117   \n",
      "2020-01-02 18:00:00   7360.65888  31.369002 -0.710850    -0.478589  -0.232261   \n",
      "2020-01-02 19:00:00   4747.08642  32.909162 -0.756788    -0.534229  -0.222559   \n",
      "2020-01-02 20:00:00   5796.42079  30.316621 -0.820850    -0.591553  -0.229297   \n",
      "2020-01-02 21:00:00   2225.95265  36.839907 -0.817015    -0.636646  -0.180369   \n",
      "2020-01-02 22:00:00   2654.51024  34.774037 -0.831024    -0.675521  -0.155502   \n",
      "2020-01-02 23:00:00   2980.68108  33.546234 -0.848483    -0.710114  -0.138370   \n",
      "\n",
      "                            cci         dx  \n",
      "time                                        \n",
      "2020-01-02 04:00:00 -107.775281  31.831321  \n",
      "2020-01-02 05:00:00  -83.458758  31.831321  \n",
      "2020-01-02 06:00:00  -78.308105  25.233499  \n",
      "2020-01-02 07:00:00  -85.151277  28.293135  \n",
      "2020-01-02 08:00:00  -33.478894  12.619851  \n",
      "2020-01-02 09:00:00  -23.124126  12.619851  \n",
      "2020-01-02 10:00:00  -15.208691  10.788518  \n",
      "2020-01-02 11:00:00  -35.657051  15.442391  \n",
      "2020-01-02 12:00:00    6.893296  13.986995  \n",
      "2020-01-02 13:00:00  -64.547896  26.381010  \n",
      "2020-01-02 14:00:00  -43.921569  26.381010  \n",
      "2020-01-02 15:00:00   -0.266870  26.381010  \n",
      "2020-01-02 16:00:00 -319.391635  56.757452  \n",
      "2020-01-02 17:00:00 -295.566321  60.789340  \n",
      "2020-01-02 18:00:00 -166.627558  58.796226  \n",
      "2020-01-02 19:00:00 -123.397792  59.185403  \n",
      "2020-01-02 20:00:00 -111.749560  61.459160  \n",
      "2020-01-02 21:00:00  -72.347677  54.125554  \n",
      "2020-01-02 22:00:00  -68.144606  54.125554  \n",
      "2020-01-02 23:00:00  -74.355556  57.349132  \n"
     ]
    }
   ],
   "source": [
    "# Drop unecessary columns and make time as index\n",
    "processed_df.index=pd.to_datetime(processed_df.time)\n",
    "processed_df.drop('time', inplace=True, axis=1)\n",
    "print(processed_df.tail(20))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "59eD5HUo1axI"
   },
   "source": [
    "# Part 2: Triple barrier method/Data Labeling\n",
    "Introduction here:\n",
    "\n",
    "https://www.youtube.com/watch?v=U2CxilKFue4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3-Rvx0yzvt-B"
   },
   "source": [
    "## 2.1 Add volatility\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "P2K6t6T51dxr"
   },
   "outputs": [],
   "source": [
    "def get_vol(prices, span=100):\n",
    "    # 1. compute returns of the form p[t]/p[t-1] - 1\n",
    "    df0 = prices.pct_change()\n",
    "    # 2. estimate rolling standard deviation\n",
    "    df0 = df0.ewm(span=span).std()\n",
    "    return df0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "IRSOgoCcsHmU"
   },
   "outputs": [],
   "source": [
    "data_ohlcv = processed_df.assign(volatility=get_vol(processed_df.close)).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 786
    },
    "id": "g63UkMTCCkLS",
    "outputId": "7ab70fe5-9c3e-4169-fcb5-128b0227a1ec"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-7fa5d533-2ee9-4f06-9fe9-8e85cb80ad0b\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tic</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adjusted_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>rsi</th>\n",
       "      <th>macd</th>\n",
       "      <th>macd_signal</th>\n",
       "      <th>macd_hist</th>\n",
       "      <th>cci</th>\n",
       "      <th>dx</th>\n",
       "      <th>volatility</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-08-18 13:00:00</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>307.17</td>\n",
       "      <td>309.66</td>\n",
       "      <td>301.30</td>\n",
       "      <td>303.22</td>\n",
       "      <td>303.22</td>\n",
       "      <td>357.19041</td>\n",
       "      <td>48.192395</td>\n",
       "      <td>-0.311981</td>\n",
       "      <td>-0.966460</td>\n",
       "      <td>0.654479</td>\n",
       "      <td>4.297256</td>\n",
       "      <td>16.280234</td>\n",
       "      <td>0.008617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 14:00:00</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>303.22</td>\n",
       "      <td>303.22</td>\n",
       "      <td>296.32</td>\n",
       "      <td>298.52</td>\n",
       "      <td>298.52</td>\n",
       "      <td>398.64644</td>\n",
       "      <td>40.780885</td>\n",
       "      <td>-0.845623</td>\n",
       "      <td>-0.942293</td>\n",
       "      <td>0.096669</td>\n",
       "      <td>-126.029338</td>\n",
       "      <td>30.900000</td>\n",
       "      <td>0.008987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 15:00:00</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>298.52</td>\n",
       "      <td>300.99</td>\n",
       "      <td>297.32</td>\n",
       "      <td>298.80</td>\n",
       "      <td>298.80</td>\n",
       "      <td>214.98578</td>\n",
       "      <td>41.359475</td>\n",
       "      <td>-1.231746</td>\n",
       "      <td>-1.000183</td>\n",
       "      <td>-0.231563</td>\n",
       "      <td>-129.416979</td>\n",
       "      <td>30.900000</td>\n",
       "      <td>0.008810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 16:00:00</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>298.80</td>\n",
       "      <td>301.11</td>\n",
       "      <td>295.64</td>\n",
       "      <td>299.21</td>\n",
       "      <td>299.21</td>\n",
       "      <td>403.96386</td>\n",
       "      <td>42.249243</td>\n",
       "      <td>-1.487521</td>\n",
       "      <td>-1.097651</td>\n",
       "      <td>-0.389870</td>\n",
       "      <td>-124.250309</td>\n",
       "      <td>35.319138</td>\n",
       "      <td>0.008643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 17:00:00</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>297.50</td>\n",
       "      <td>299.21</td>\n",
       "      <td>293.18</td>\n",
       "      <td>295.53</td>\n",
       "      <td>295.53</td>\n",
       "      <td>359.12225</td>\n",
       "      <td>36.845308</td>\n",
       "      <td>-1.964524</td>\n",
       "      <td>-1.271026</td>\n",
       "      <td>-0.693498</td>\n",
       "      <td>-146.917109</td>\n",
       "      <td>41.244545</td>\n",
       "      <td>0.008778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 18:00:00</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>295.53</td>\n",
       "      <td>298.25</td>\n",
       "      <td>289.75</td>\n",
       "      <td>289.75</td>\n",
       "      <td>289.75</td>\n",
       "      <td>412.39491</td>\n",
       "      <td>30.291711</td>\n",
       "      <td>-2.776939</td>\n",
       "      <td>-1.572208</td>\n",
       "      <td>-1.204731</td>\n",
       "      <td>-164.397203</td>\n",
       "      <td>48.349490</td>\n",
       "      <td>0.009322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 19:00:00</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>289.75</td>\n",
       "      <td>295.60</td>\n",
       "      <td>287.37</td>\n",
       "      <td>293.83</td>\n",
       "      <td>293.83</td>\n",
       "      <td>445.23397</td>\n",
       "      <td>38.594463</td>\n",
       "      <td>-3.056330</td>\n",
       "      <td>-1.869033</td>\n",
       "      <td>-1.187298</td>\n",
       "      <td>-130.540062</td>\n",
       "      <td>52.629890</td>\n",
       "      <td>0.009623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 20:00:00</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>295.46</td>\n",
       "      <td>295.46</td>\n",
       "      <td>288.25</td>\n",
       "      <td>291.87</td>\n",
       "      <td>291.87</td>\n",
       "      <td>748.21537</td>\n",
       "      <td>36.354327</td>\n",
       "      <td>-3.396750</td>\n",
       "      <td>-2.174576</td>\n",
       "      <td>-1.222174</td>\n",
       "      <td>-112.032161</td>\n",
       "      <td>52.629890</td>\n",
       "      <td>0.009505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 21:00:00</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>291.87</td>\n",
       "      <td>291.87</td>\n",
       "      <td>283.94</td>\n",
       "      <td>286.30</td>\n",
       "      <td>286.30</td>\n",
       "      <td>602.03512</td>\n",
       "      <td>30.870574</td>\n",
       "      <td>-4.069081</td>\n",
       "      <td>-2.553477</td>\n",
       "      <td>-1.515604</td>\n",
       "      <td>-139.176779</td>\n",
       "      <td>59.652466</td>\n",
       "      <td>0.009911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 22:00:00</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>288.01</td>\n",
       "      <td>294.19</td>\n",
       "      <td>284.02</td>\n",
       "      <td>293.74</td>\n",
       "      <td>293.74</td>\n",
       "      <td>477.84453</td>\n",
       "      <td>43.196028</td>\n",
       "      <td>-3.955961</td>\n",
       "      <td>-2.833974</td>\n",
       "      <td>-1.121987</td>\n",
       "      <td>-90.175842</td>\n",
       "      <td>47.017971</td>\n",
       "      <td>0.011025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7fa5d533-2ee9-4f06-9fe9-8e85cb80ad0b')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-7fa5d533-2ee9-4f06-9fe9-8e85cb80ad0b button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-7fa5d533-2ee9-4f06-9fe9-8e85cb80ad0b');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                         tic    open    high     low   close  adjusted_close  \\\n",
       "time                                                                           \n",
       "2017-08-18 13:00:00  ETHUSDT  307.17  309.66  301.30  303.22          303.22   \n",
       "2017-08-18 14:00:00  ETHUSDT  303.22  303.22  296.32  298.52          298.52   \n",
       "2017-08-18 15:00:00  ETHUSDT  298.52  300.99  297.32  298.80          298.80   \n",
       "2017-08-18 16:00:00  ETHUSDT  298.80  301.11  295.64  299.21          299.21   \n",
       "2017-08-18 17:00:00  ETHUSDT  297.50  299.21  293.18  295.53          295.53   \n",
       "2017-08-18 18:00:00  ETHUSDT  295.53  298.25  289.75  289.75          289.75   \n",
       "2017-08-18 19:00:00  ETHUSDT  289.75  295.60  287.37  293.83          293.83   \n",
       "2017-08-18 20:00:00  ETHUSDT  295.46  295.46  288.25  291.87          291.87   \n",
       "2017-08-18 21:00:00  ETHUSDT  291.87  291.87  283.94  286.30          286.30   \n",
       "2017-08-18 22:00:00  ETHUSDT  288.01  294.19  284.02  293.74          293.74   \n",
       "\n",
       "                        volume        rsi      macd  macd_signal  macd_hist  \\\n",
       "time                                                                          \n",
       "2017-08-18 13:00:00  357.19041  48.192395 -0.311981    -0.966460   0.654479   \n",
       "2017-08-18 14:00:00  398.64644  40.780885 -0.845623    -0.942293   0.096669   \n",
       "2017-08-18 15:00:00  214.98578  41.359475 -1.231746    -1.000183  -0.231563   \n",
       "2017-08-18 16:00:00  403.96386  42.249243 -1.487521    -1.097651  -0.389870   \n",
       "2017-08-18 17:00:00  359.12225  36.845308 -1.964524    -1.271026  -0.693498   \n",
       "2017-08-18 18:00:00  412.39491  30.291711 -2.776939    -1.572208  -1.204731   \n",
       "2017-08-18 19:00:00  445.23397  38.594463 -3.056330    -1.869033  -1.187298   \n",
       "2017-08-18 20:00:00  748.21537  36.354327 -3.396750    -2.174576  -1.222174   \n",
       "2017-08-18 21:00:00  602.03512  30.870574 -4.069081    -2.553477  -1.515604   \n",
       "2017-08-18 22:00:00  477.84453  43.196028 -3.955961    -2.833974  -1.121987   \n",
       "\n",
       "                            cci         dx  volatility  \n",
       "time                                                    \n",
       "2017-08-18 13:00:00    4.297256  16.280234    0.008617  \n",
       "2017-08-18 14:00:00 -126.029338  30.900000    0.008987  \n",
       "2017-08-18 15:00:00 -129.416979  30.900000    0.008810  \n",
       "2017-08-18 16:00:00 -124.250309  35.319138    0.008643  \n",
       "2017-08-18 17:00:00 -146.917109  41.244545    0.008778  \n",
       "2017-08-18 18:00:00 -164.397203  48.349490    0.009322  \n",
       "2017-08-18 19:00:00 -130.540062  52.629890    0.009623  \n",
       "2017-08-18 20:00:00 -112.032161  52.629890    0.009505  \n",
       "2017-08-18 21:00:00 -139.176779  59.652466    0.009911  \n",
       "2017-08-18 22:00:00  -90.175842  47.017971    0.011025  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_ohlcv.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zGa9sSGd_GdK"
   },
   "source": [
    "## 2.2 Adding Path Dependency: Triple-Barrier Method\n",
    "\n",
    "To better incorporate the stop-loss and take-profit scenarios of a hypothetical trading strategy, we will modify the fixed-horizon labeling method so that it reflects which barrier has been touched first — upper, lower, or horizon. Hence the name: the triple-barrier method.\n",
    "\n",
    "The labeling schema is defined as follows:\n",
    "\n",
    "* y = 2 : top barrier is hit first\n",
    "* y = 1 : right barrier is hit first\n",
    "* y = 0 : bottom barrier is hit first\n",
    "\n",
    "What about the side of the bet?\n",
    "\n",
    "The schema above works fine for long-only strategies, however things get more complicated when we allow for both long and short bets. If we are betting short, our profit/loss is inverted relative to the price action — we profit if the price goes down and we lose when the price goes up.\n",
    "\n",
    "In order to account for this, we can simply represent side as 2 for long and 0 for short. Thus we can multiply our returns by the side, so whenever we’re betting short the negative returns become positive and vice-versa. Effectively, we flip the y = 2 and y = 0 labels if side = 0.\n",
    "\n",
    "Let’s take a shot at the implementation (based on MLDP’s code).\n",
    "First, we define the procedure for getting the timestamps of the horizon barriers:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5VoiqRnSCIJd"
   },
   "source": [
    "### Create function to obtain the barrier hits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "9OUo9mkeCKny"
   },
   "outputs": [],
   "source": [
    "def get_barriers():\n",
    "  #create a container\n",
    "  barriers = pd.DataFrame(columns=['days_passed', \n",
    "            'price', 'vert_barrier', \\\n",
    "            'top_barrier', 'bottom_barrier'], \\\n",
    "              index = daily_volatility.index)\n",
    "  for day, vol in daily_volatility.iteritems():\n",
    "    days_passed = len(daily_volatility.loc \\\n",
    "                  [daily_volatility.index[0] : day])\n",
    "    #set the vertical barrier \n",
    "    if (days_passed + t_final < len(daily_volatility.index) \\\n",
    "        and t_final != 0):\n",
    "        vert_barrier = daily_volatility.index[\n",
    "                            days_passed + t_final]\n",
    "    else:\n",
    "        vert_barrier = np.nan\n",
    "    #set the top barrier\n",
    "    if upper_lower_multipliers[0] > 0:\n",
    "        top_barrier = prices.loc[day] + prices.loc[day] * \\\n",
    "                      upper_lower_multipliers[0] * vol\n",
    "    else:\n",
    "        #set it to NaNs\n",
    "        top_barrier = pd.Series(index=prices.index)\n",
    "    #set the bottom barrier\n",
    "    if upper_lower_multipliers[1] > 0:\n",
    "        bottom_barrier = prices.loc[day] - prices.loc[day] * \\\n",
    "                      upper_lower_multipliers[1] * vol\n",
    "    else: \n",
    "        #set it to NaNs\n",
    "        bottom_barrier = pd.Series(index=prices.index)\n",
    "        \n",
    "    barriers.loc[day, ['days_passed', 'price', 'vert_barrier','top_barrier', 'bottom_barrier']] = \\\n",
    "    days_passed, prices.loc[day], vert_barrier, top_barrier, bottom_barrier\n",
    "\n",
    "  return barriers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "6gh0tSPaCnNZ"
   },
   "outputs": [],
   "source": [
    "# Set barrier parameters\n",
    "\n",
    "daily_volatility = data_ohlcv['volatility']\n",
    "t_final = 25\n",
    "upper_lower_multipliers = [2, 2]\n",
    "price = data_ohlcv['close']\n",
    "prices = price[daily_volatility.index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 455
    },
    "id": "KBvziyM1FqAa",
    "outputId": "8c1bb7be-5b81-4752-dd51-390d6c7b8642"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-6d01679e-6fb6-4da2-bc48-c7b4d7547434\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>days_passed</th>\n",
       "      <th>price</th>\n",
       "      <th>vert_barrier</th>\n",
       "      <th>top_barrier</th>\n",
       "      <th>bottom_barrier</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-08-18 13:00:00</th>\n",
       "      <td>1</td>\n",
       "      <td>303.22</td>\n",
       "      <td>2017-08-19 15:00:00</td>\n",
       "      <td>308.445986</td>\n",
       "      <td>297.994014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 14:00:00</th>\n",
       "      <td>2</td>\n",
       "      <td>298.52</td>\n",
       "      <td>2017-08-19 16:00:00</td>\n",
       "      <td>303.885375</td>\n",
       "      <td>293.154625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 15:00:00</th>\n",
       "      <td>3</td>\n",
       "      <td>298.8</td>\n",
       "      <td>2017-08-19 17:00:00</td>\n",
       "      <td>304.064768</td>\n",
       "      <td>293.535232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 16:00:00</th>\n",
       "      <td>4</td>\n",
       "      <td>299.21</td>\n",
       "      <td>2017-08-19 18:00:00</td>\n",
       "      <td>304.382229</td>\n",
       "      <td>294.037771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 17:00:00</th>\n",
       "      <td>5</td>\n",
       "      <td>295.53</td>\n",
       "      <td>2017-08-19 19:00:00</td>\n",
       "      <td>300.718237</td>\n",
       "      <td>290.341763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 19:00:00</th>\n",
       "      <td>20264</td>\n",
       "      <td>127.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128.806992</td>\n",
       "      <td>126.433008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 20:00:00</th>\n",
       "      <td>20265</td>\n",
       "      <td>127.16</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128.336537</td>\n",
       "      <td>125.983463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 21:00:00</th>\n",
       "      <td>20266</td>\n",
       "      <td>127.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128.902774</td>\n",
       "      <td>126.537226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 22:00:00</th>\n",
       "      <td>20267</td>\n",
       "      <td>127.39</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128.560684</td>\n",
       "      <td>126.219316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-02 23:00:00</th>\n",
       "      <td>20268</td>\n",
       "      <td>127.19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>128.347967</td>\n",
       "      <td>126.032033</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20268 rows × 5 columns</p>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6d01679e-6fb6-4da2-bc48-c7b4d7547434')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-6d01679e-6fb6-4da2-bc48-c7b4d7547434 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-6d01679e-6fb6-4da2-bc48-c7b4d7547434');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                    days_passed   price         vert_barrier top_barrier  \\\n",
       "time                                                                       \n",
       "2017-08-18 13:00:00           1  303.22  2017-08-19 15:00:00  308.445986   \n",
       "2017-08-18 14:00:00           2  298.52  2017-08-19 16:00:00  303.885375   \n",
       "2017-08-18 15:00:00           3   298.8  2017-08-19 17:00:00  304.064768   \n",
       "2017-08-18 16:00:00           4  299.21  2017-08-19 18:00:00  304.382229   \n",
       "2017-08-18 17:00:00           5  295.53  2017-08-19 19:00:00  300.718237   \n",
       "...                         ...     ...                  ...         ...   \n",
       "2020-01-02 19:00:00       20264  127.62                  NaN  128.806992   \n",
       "2020-01-02 20:00:00       20265  127.16                  NaN  128.336537   \n",
       "2020-01-02 21:00:00       20266  127.72                  NaN  128.902774   \n",
       "2020-01-02 22:00:00       20267  127.39                  NaN  128.560684   \n",
       "2020-01-02 23:00:00       20268  127.19                  NaN  128.347967   \n",
       "\n",
       "                    bottom_barrier  \n",
       "time                                \n",
       "2017-08-18 13:00:00     297.994014  \n",
       "2017-08-18 14:00:00     293.154625  \n",
       "2017-08-18 15:00:00     293.535232  \n",
       "2017-08-18 16:00:00     294.037771  \n",
       "2017-08-18 17:00:00     290.341763  \n",
       "...                            ...  \n",
       "2020-01-02 19:00:00     126.433008  \n",
       "2020-01-02 20:00:00     125.983463  \n",
       "2020-01-02 21:00:00     126.537226  \n",
       "2020-01-02 22:00:00     126.219316  \n",
       "2020-01-02 23:00:00     126.032033  \n",
       "\n",
       "[20268 rows x 5 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "barriers = get_barriers()\n",
    "barriers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "FktzhO5gChKq"
   },
   "source": [
    "## 2.3 Function to get label for the dataset (0, 1, 2)\n",
    "\n",
    "* 0: hit the stoploss\n",
    "* 1: hit the time out\n",
    "* 2: hit the profit take\n",
    "\n",
    "The part in this function (commented out), allows for easy conversion to a regression analysis (currently it is classification). If one changes the labels to (-1, 0, 1), and change the hit on the vertical barrier to the function stated below.\n",
    "\n",
    "That will make hitting the profit take barrier 1, the vertical barrier a range from (-1, 1), and the stoploss barrier -1. This is a continuos space then.\n",
    "\n",
    "```\n",
    "barriers['out'][i] = max(\n",
    "          [(price_final - price_initial)/ \n",
    "            (top_barrier - price_initial), \\\n",
    "            (price_final - price_initial)/ \\\n",
    "            (price_initial - bottom_barrier)],\\\n",
    "            key=abs)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "id": "fHNWUpFPGAcO"
   },
   "outputs": [],
   "source": [
    "def get_labels():\n",
    "  '''\n",
    "  start: first day of the window\n",
    "  end:last day of the window\n",
    "  price_initial: first day stock price\n",
    "  price_final:last day stock price\n",
    "  top_barrier: profit taking limit\n",
    "  bottom_barrier:stop loss limt\n",
    "  condition_pt:top_barrier touching conditon\n",
    "  condition_sl:bottom_barrier touching conditon\n",
    "  '''\n",
    "\n",
    "  barriers[\"label_barrier\"] = None\n",
    "  for i in range(len(barriers.index)):\n",
    "    start = barriers.index[i]\n",
    "    end = barriers.vert_barrier[i]\n",
    "    if pd.notna(end):\n",
    "\n",
    "        # assign the initial and final price\n",
    "        price_initial = barriers.price[start]\n",
    "        price_final = barriers.price[end]\n",
    "\n",
    "        # assign the top and bottom barriers\n",
    "        top_barrier = barriers.top_barrier[i]\n",
    "        bottom_barrier = barriers.bottom_barrier[i]\n",
    "\n",
    "        #set the profit taking and stop loss conditons\n",
    "        condition_pt = (barriers.price[start: end] >= \\\n",
    "          top_barrier).any()\n",
    "        condition_sl = (barriers.price[start: end] <= \\\n",
    "          bottom_barrier).any()\n",
    "\n",
    "        #assign the labels\n",
    "        if condition_pt: \n",
    "            barriers['label_barrier'][i] = 2\n",
    "        elif condition_sl: \n",
    "            barriers['label_barrier'][i] = 0    \n",
    "        else: \n",
    "\n",
    "          # Change to regression analysis by switching labels (-1, 0, 1)\n",
    "          # and uncommenting the alternative function for vert barrier\n",
    "\n",
    "          barriers['label_barrier'][i] = 1\n",
    "            # barriers['label_barrier'][i] = max(\n",
    "            #           [(price_final - price_initial)/ \n",
    "            #             (top_barrier - price_initial), \\\n",
    "            #             (price_final - price_initial)/ \\\n",
    "            #             (price_initial - bottom_barrier)],\\\n",
    "            #             key=abs)\n",
    "\n",
    "  return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 475
    },
    "id": "aUq71tykusGG",
    "outputId": "59dba93f-73e4-46a7-f44f-a28c4cda6ccf"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-d902d8b8-4a61-4fb1-a35f-c277638118f0\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tic</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adjusted_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>rsi</th>\n",
       "      <th>macd</th>\n",
       "      <th>macd_signal</th>\n",
       "      <th>macd_hist</th>\n",
       "      <th>cci</th>\n",
       "      <th>dx</th>\n",
       "      <th>volatility</th>\n",
       "      <th>vert_barrier</th>\n",
       "      <th>top_barrier</th>\n",
       "      <th>bottom_barrier</th>\n",
       "      <th>label_barrier</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-08-18 13:00:00</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>307.17</td>\n",
       "      <td>309.66</td>\n",
       "      <td>301.30</td>\n",
       "      <td>303.22</td>\n",
       "      <td>303.22</td>\n",
       "      <td>357.19041</td>\n",
       "      <td>48.192395</td>\n",
       "      <td>-0.311981</td>\n",
       "      <td>-0.966460</td>\n",
       "      <td>0.654479</td>\n",
       "      <td>4.297256</td>\n",
       "      <td>16.280234</td>\n",
       "      <td>0.008617</td>\n",
       "      <td>2017-08-19 15:00:00</td>\n",
       "      <td>308.445986</td>\n",
       "      <td>297.994014</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 14:00:00</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>303.22</td>\n",
       "      <td>303.22</td>\n",
       "      <td>296.32</td>\n",
       "      <td>298.52</td>\n",
       "      <td>298.52</td>\n",
       "      <td>398.64644</td>\n",
       "      <td>40.780885</td>\n",
       "      <td>-0.845623</td>\n",
       "      <td>-0.942293</td>\n",
       "      <td>0.096669</td>\n",
       "      <td>-126.029338</td>\n",
       "      <td>30.900000</td>\n",
       "      <td>0.008987</td>\n",
       "      <td>2017-08-19 16:00:00</td>\n",
       "      <td>303.885375</td>\n",
       "      <td>293.154625</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 15:00:00</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>298.52</td>\n",
       "      <td>300.99</td>\n",
       "      <td>297.32</td>\n",
       "      <td>298.80</td>\n",
       "      <td>298.80</td>\n",
       "      <td>214.98578</td>\n",
       "      <td>41.359475</td>\n",
       "      <td>-1.231746</td>\n",
       "      <td>-1.000183</td>\n",
       "      <td>-0.231563</td>\n",
       "      <td>-129.416979</td>\n",
       "      <td>30.900000</td>\n",
       "      <td>0.008810</td>\n",
       "      <td>2017-08-19 17:00:00</td>\n",
       "      <td>304.064768</td>\n",
       "      <td>293.535232</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 16:00:00</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>298.80</td>\n",
       "      <td>301.11</td>\n",
       "      <td>295.64</td>\n",
       "      <td>299.21</td>\n",
       "      <td>299.21</td>\n",
       "      <td>403.96386</td>\n",
       "      <td>42.249243</td>\n",
       "      <td>-1.487521</td>\n",
       "      <td>-1.097651</td>\n",
       "      <td>-0.389870</td>\n",
       "      <td>-124.250309</td>\n",
       "      <td>35.319138</td>\n",
       "      <td>0.008643</td>\n",
       "      <td>2017-08-19 18:00:00</td>\n",
       "      <td>304.382229</td>\n",
       "      <td>294.037771</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-08-18 17:00:00</th>\n",
       "      <td>ETHUSDT</td>\n",
       "      <td>297.50</td>\n",
       "      <td>299.21</td>\n",
       "      <td>293.18</td>\n",
       "      <td>295.53</td>\n",
       "      <td>295.53</td>\n",
       "      <td>359.12225</td>\n",
       "      <td>36.845308</td>\n",
       "      <td>-1.964524</td>\n",
       "      <td>-1.271026</td>\n",
       "      <td>-0.693498</td>\n",
       "      <td>-146.917109</td>\n",
       "      <td>41.244545</td>\n",
       "      <td>0.008778</td>\n",
       "      <td>2017-08-19 19:00:00</td>\n",
       "      <td>300.718237</td>\n",
       "      <td>290.341763</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d902d8b8-4a61-4fb1-a35f-c277638118f0')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-d902d8b8-4a61-4fb1-a35f-c277638118f0 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-d902d8b8-4a61-4fb1-a35f-c277638118f0');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "                         tic    open    high     low   close  adjusted_close  \\\n",
       "time                                                                           \n",
       "2017-08-18 13:00:00  ETHUSDT  307.17  309.66  301.30  303.22          303.22   \n",
       "2017-08-18 14:00:00  ETHUSDT  303.22  303.22  296.32  298.52          298.52   \n",
       "2017-08-18 15:00:00  ETHUSDT  298.52  300.99  297.32  298.80          298.80   \n",
       "2017-08-18 16:00:00  ETHUSDT  298.80  301.11  295.64  299.21          299.21   \n",
       "2017-08-18 17:00:00  ETHUSDT  297.50  299.21  293.18  295.53          295.53   \n",
       "\n",
       "                        volume        rsi      macd  macd_signal  macd_hist  \\\n",
       "time                                                                          \n",
       "2017-08-18 13:00:00  357.19041  48.192395 -0.311981    -0.966460   0.654479   \n",
       "2017-08-18 14:00:00  398.64644  40.780885 -0.845623    -0.942293   0.096669   \n",
       "2017-08-18 15:00:00  214.98578  41.359475 -1.231746    -1.000183  -0.231563   \n",
       "2017-08-18 16:00:00  403.96386  42.249243 -1.487521    -1.097651  -0.389870   \n",
       "2017-08-18 17:00:00  359.12225  36.845308 -1.964524    -1.271026  -0.693498   \n",
       "\n",
       "                            cci         dx  volatility         vert_barrier  \\\n",
       "time                                                                          \n",
       "2017-08-18 13:00:00    4.297256  16.280234    0.008617  2017-08-19 15:00:00   \n",
       "2017-08-18 14:00:00 -126.029338  30.900000    0.008987  2017-08-19 16:00:00   \n",
       "2017-08-18 15:00:00 -129.416979  30.900000    0.008810  2017-08-19 17:00:00   \n",
       "2017-08-18 16:00:00 -124.250309  35.319138    0.008643  2017-08-19 18:00:00   \n",
       "2017-08-18 17:00:00 -146.917109  41.244545    0.008778  2017-08-19 19:00:00   \n",
       "\n",
       "                    top_barrier bottom_barrier label_barrier  \n",
       "time                                                          \n",
       "2017-08-18 13:00:00  308.445986     297.994014             0  \n",
       "2017-08-18 14:00:00  303.885375     293.154625             0  \n",
       "2017-08-18 15:00:00  304.064768     293.535232             0  \n",
       "2017-08-18 16:00:00  304.382229     294.037771             0  \n",
       "2017-08-18 17:00:00  300.718237     290.341763             0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use function to produce barriers\n",
    "\n",
    "get_labels()\n",
    "barriers\n",
    "\n",
    "# Merge the barriers with the main dataset and drop the last t_final + 1 barriers (as they are too close to the end)\n",
    "\n",
    "data_ohlcv = data_ohlcv.merge(barriers[['vert_barrier', 'top_barrier', 'bottom_barrier', 'label_barrier']], left_on='time', right_on='time')\n",
    "data_ohlcv.drop(data_ohlcv.tail(t_final + 1).index,inplace = True)\n",
    "data_ohlcv.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZiteMW0qGw8p",
    "outputId": "15df025f-857a-491d-bde6-55b201c273bb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    9971\n",
       "0    7956\n",
       "1    2315\n",
       "Name: label_barrier, dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count barrier hits ( 0 = stoploss, 1 = timeout, 2 = profit take)\n",
    "pd.Series(data_ohlcv['label_barrier']).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 631
    },
    "id": "4OUIDcJrHLkf",
    "outputId": "cac17e31-b19b-4791-96dc-95aa8ec8208c"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 6000x2400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "plt.xticks(rotation=45)\n",
    "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%m/%d/%Y'))\n",
    "plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=1))\n",
    "\n",
    "\n",
    "TIMESTAMP_TO_PLOT= 300\n",
    "\n",
    "ax.set(title='ETH/USDT',\n",
    "       xlabel='date', ylabel='price')\n",
    "ax.plot(data_ohlcv.close[200:600])\n",
    "\n",
    "start = data_ohlcv.index[TIMESTAMP_TO_PLOT]\n",
    "end = data_ohlcv.vert_barrier[TIMESTAMP_TO_PLOT]\n",
    "upper_barrier = data_ohlcv.top_barrier[TIMESTAMP_TO_PLOT]\n",
    "lower_barrier = data_ohlcv.bottom_barrier[TIMESTAMP_TO_PLOT]\n",
    "\n",
    "ax.plot([start, end], [upper_barrier, upper_barrier], 'r--');\n",
    "ax.plot([start, end], [lower_barrier, lower_barrier], 'r--');\n",
    "ax.plot([start, end], [(lower_barrier + upper_barrier)*0.5, \\\n",
    "                       (lower_barrier + upper_barrier)*0.5], 'r--');\n",
    "ax.plot([start, start], [lower_barrier, upper_barrier], 'r-');\n",
    "ax.plot([end, end], [lower_barrier, upper_barrier], 'r-');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TdVqwKVKxetM"
   },
   "source": [
    "# Part 3: Copying the Neural Network present in ElegantRL ActorPPO agent.\n",
    "\n",
    "In ElegantRL from AI4Finance, all the preprogrammed Agents are present:\n",
    "\n",
    "https://github.com/AI4Finance-Foundation/ElegantRL/blob/master/elegantrl/agents/net.py\n",
    "\n",
    "Some of the actions output discrete actions (classification), and some continuous actions (regression). This notebook can be adapted for both. by turning the labeling method in a (-1, 0, 1) and changing the Neural network to output a continuous space between -1 and 1.\n",
    "\n",
    "Therefore this notebook allows for analysis for both regression and classification.\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "yiCj3qRwkfXF"
   },
   "outputs": [],
   "source": [
    "data_ohlcv = data_ohlcv.drop(['vert_barrier', 'top_barrier', 'bottom_barrier','adjusted_close','tic'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5ImrJ30EwZo3",
    "outputId": "513d196f-67aa-42f2-d5d1-78fa0be252fb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Net(\n",
      "  (fc1): Linear(in_features=12, out_features=1024, bias=True)\n",
      "  (relu1): ReLU()\n",
      "  (fc2): Linear(in_features=1024, out_features=1024, bias=True)\n",
      "  (relu2): ReLU()\n",
      "  (fc3): Linear(in_features=1024, out_features=1024, bias=True)\n",
      "  (hw1): Hardswish()\n",
      "  (fc_out): Linear(in_features=1024, out_features=3, bias=True)\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "class Net(nn.Module):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(Net, self).__init__()\n",
    "\n",
    "        self.state_dim = 12       # all the features\n",
    "        self.mid_dim = 2**10      # net dimension\n",
    "        self.action_dim = 3       # output (sell/nothing/buy)\n",
    "\n",
    "        # make a copy of the model in ActorPPO (activation function in forward function)\n",
    "\n",
    "        # Original initial layers\n",
    "        self.fc1 = nn.Linear(self.state_dim, self.mid_dim)\n",
    "        self.relu1 = nn.ReLU()\n",
    "        self.fc2 = nn.Linear(self.mid_dim, self.mid_dim)\n",
    "\n",
    "        # Original residual layers\n",
    "        self.relu2 = nn.ReLU()\n",
    "        self.fc3 = nn.Linear(self.mid_dim, self.mid_dim)\n",
    "        self.hw1 = nn.Hardswish()\n",
    "        self.fc_out = nn.Linear(self.mid_dim, self.action_dim)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.float()\n",
    "\n",
    "        # Original initial layers\n",
    "        x = self.fc1(x)\n",
    "        x = self.relu1(x)\n",
    "        x = self.fc2(x)\n",
    "\n",
    "        # Original residual layers\n",
    "        x = self.relu2(x)\n",
    "        x = self.fc3(x)\n",
    "        x = self.hw1(x)\n",
    "        x = self.fc_out(x)\n",
    "        return x\n",
    "\n",
    "model_NN1 = Net()\n",
    "print(model_NN1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "8Ve3uwKv3pSw"
   },
   "outputs": [],
   "source": [
    "class ClassifierDataset(Dataset):\n",
    "    \n",
    "    def __init__(self, X_data, y_data):\n",
    "        self.X_data = X_data\n",
    "        self.y_data = y_data\n",
    "        \n",
    "    def __getitem__(self, index):\n",
    "        return self.X_data[index], self.y_data[index]\n",
    "        \n",
    "    def __len__ (self):\n",
    "        return len(self.X_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fBgkfxwmwBgc"
   },
   "source": [
    "## 3.1 Set constants and train/test split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "6Oh0fsz7JMcs"
   },
   "outputs": [],
   "source": [
    "# Set constants\n",
    "batch_size=16\n",
    "epochs=300\n",
    "\n",
    "# Reinitiating data here\n",
    "data = data_ohlcv\n",
    "\n",
    "X = data[['open', 'high', 'low', 'close', 'volume', 'rsi', 'macd', 'macd_signal', 'macd_hist', 'cci', 'dx', 'volatility']].values\n",
    "y = np.squeeze(data[['label_barrier']].values).astype(int)\n",
    "\n",
    "# Split into train+val and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=69)\n",
    "\n",
    "# Normalize input\n",
    "scaler = MinMaxScaler()\n",
    "X_train = scaler.fit_transform(X_train)\n",
    "X_test = scaler.transform(X_test)\n",
    "\n",
    "# Convert to numpy arrays\n",
    "X_train, y_train = np.array(X_train), np.array(y_train)\n",
    "X_test, y_test = np.array(X_test), np.array(y_test)\n",
    "\n",
    "# initialize sets and convet them to Pytorch dataloader sets\n",
    "train_dataset = ClassifierDataset(torch.from_numpy(X_train).float(), torch.from_numpy(y_train.astype(int)).long())\n",
    "test_dataset = ClassifierDataset(torch.from_numpy(X_test).float(), torch.from_numpy(y_test.astype(int)).long())\n",
    "\n",
    "\n",
    "train_loader = DataLoader(dataset=train_dataset,\n",
    "                          batch_size=batch_size\n",
    "                          )\n",
    "\n",
    "test_loader = DataLoader(dataset=test_dataset, \n",
    "                         batch_size=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UsDj1N0Zh1ON"
   },
   "source": [
    "## 3.2 Check if GPU availabble"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XKUIcIAZ7fb4",
    "outputId": "47a87d9e-e8e6-4e6c-a71a-95f7456359cf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "# Check GPU\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)\n",
    "\n",
    "# Set optimizer\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.Adam(model_NN1.parameters(), lr=0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "WdAUenBzav9s",
    "outputId": "c45448ca-5902-4548-8210-3a1a9481ac22"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sun May  8 13:46:54 2022       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 460.32.03    Driver Version: 460.32.03    CUDA Version: 11.2     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  Tesla K80           Off  | 00000000:00:04.0 Off |                    0 |\n",
      "| N/A   44C    P8    31W / 149W |      3MiB / 11441MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|  No running processes found                                                 |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "## Make sure you are working on GPU\n",
    "assert torch.cuda.is_available(), \"Change your runtime to GPU! Currently working on CPU... zzzzz\"\n",
    "\n",
    "gpu_info = !nvidia-smi\n",
    "gpu_info = '\\n'.join(gpu_info)\n",
    "if gpu_info.find('failed') >= 0:\n",
    "  print('Not connected to a GPU')\n",
    "else:\n",
    "  print(gpu_info)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aRwOVdhpUoHi"
   },
   "source": [
    "## 3.3 Now train the ```model_NN1```; as long as the test loss reduces keep on training!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "id": "VCCUHYJM3EOO"
   },
   "outputs": [],
   "source": [
    "# Train function\n",
    "def train(fold, model, device, trainloader, optimizer, epoch):\n",
    "  model.train()\n",
    "  correct_train = 0\n",
    "  correct_this_batch_train = 0\n",
    "  total_train_loss = 0\n",
    "  for batch_idx, (data, target) in enumerate(train_loader):\n",
    "      data, target = data.to(device), target.to(device)\n",
    "      optimizer.zero_grad()\n",
    "      output = model(data)\n",
    "      train_loss = criterion(output, target.flatten())\n",
    "      train_loss.backward()\n",
    "      optimizer.step()\n",
    "\n",
    "      if batch_idx % 100 == 0:\n",
    "          print('Train Fold/Epoch: {}/{} [{}/{} ({:.0f}%)]\\ttrain_loss: {:.6f}'.format(\n",
    "              fold,epoch, batch_idx * len(data), len(train_loader.dataset),\n",
    "              100. * batch_idx / len(train_loader), train_loss.item()))\n",
    "          \n",
    "      # Measure accuracy on train set\n",
    "      total_train_loss += train_loss.item()\n",
    "      _, y_pred_tags_train = torch.max(output, dim = 1)\n",
    "      correct_this_batch_train = y_pred_tags_train.eq(target.flatten().view_as(y_pred_tags_train))\n",
    "      correct_train += correct_this_batch_train.sum().item()\n",
    "  \n",
    "  return correct_train, train_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "Cw2Fd1hq3s5D"
   },
   "outputs": [],
   "source": [
    "# Test function\n",
    "def test(fold,model, device, test_loader, correct_train, train_loss):\n",
    "  model.eval()\n",
    "  test_loss = 0\n",
    "  correct = 0\n",
    "  with torch.no_grad():\n",
    "      for data, target in test_loader:\n",
    "          data, target = data.to(device), target.to(device)\n",
    "          output = model(data)\n",
    "          test_loss += criterion(output, target.flatten()).item()  # sum up batch loss\n",
    "\n",
    "          # Measure accuracy on test set\n",
    "          _, y_pred_tags = torch.max(output, dim = 1)\n",
    "          correct_this_batch = y_pred_tags.eq(target.flatten().view_as(y_pred_tags))\n",
    "          correct += correct_this_batch.sum().item() \n",
    "\n",
    "  test_loss /= len(test_loader.dataset)\n",
    "  train_loss /= len(train_loader.dataset)\n",
    "\n",
    "  # Print train accuracy for epoch\n",
    "  # TODO: still a bug in summed up batch train loss \n",
    "  print('\\nTrain set for fold {}: Average train_loss: {:.4f}, Accuracy: {}/{} ({:.5f}%)'.format(\n",
    "  fold, train_loss, correct_train, len(train_loader.dataset),\n",
    "  100 * correct_train / len(train_loader.dataset)))\n",
    "\n",
    "  # Print test result for epoch\n",
    "  print('Test set for fold {}:  Average test_loss:  {:.4f}, Accuracy: {}/{} ({:.5f}%)\\n'.format(\n",
    "      fold, test_loss, correct, len(test_loader.dataset),\n",
    "      100 * correct / len(test_loader.dataset)))  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2Itp5eg4yxhU",
    "outputId": "55d40956-26d0-401a-e7b6-cb19e3927fb8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Fold/Epoch: 0/1 [0/16193 (0%)]\ttrain_loss: 0.858524\n",
      "Train Fold/Epoch: 0/1 [1600/16193 (10%)]\ttrain_loss: 1.188345\n",
      "Train Fold/Epoch: 0/1 [3200/16193 (20%)]\ttrain_loss: 0.988540\n",
      "Train Fold/Epoch: 0/1 [4800/16193 (30%)]\ttrain_loss: 0.856347\n",
      "Train Fold/Epoch: 0/1 [6400/16193 (39%)]\ttrain_loss: 0.918068\n",
      "Train Fold/Epoch: 0/1 [8000/16193 (49%)]\ttrain_loss: 0.880552\n",
      "Train Fold/Epoch: 0/1 [9600/16193 (59%)]\ttrain_loss: 0.824769\n",
      "Train Fold/Epoch: 0/1 [11200/16193 (69%)]\ttrain_loss: 1.200953\n",
      "Train Fold/Epoch: 0/1 [12800/16193 (79%)]\ttrain_loss: 0.750023\n",
      "Train Fold/Epoch: 0/1 [14400/16193 (89%)]\ttrain_loss: 0.835101\n",
      "Train Fold/Epoch: 0/1 [16000/16193 (99%)]\ttrain_loss: 1.183760\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 8046/16193 (49.68814%)\n",
      "Test set for fold 0:  Average test_loss:  0.9473, Accuracy: 2037/4049 (50.30872%)\n",
      "\n",
      "Train Fold/Epoch: 0/2 [0/16193 (0%)]\ttrain_loss: 0.848144\n",
      "Train Fold/Epoch: 0/2 [1600/16193 (10%)]\ttrain_loss: 1.165802\n",
      "Train Fold/Epoch: 0/2 [3200/16193 (20%)]\ttrain_loss: 0.999298\n",
      "Train Fold/Epoch: 0/2 [4800/16193 (30%)]\ttrain_loss: 0.847069\n",
      "Train Fold/Epoch: 0/2 [6400/16193 (39%)]\ttrain_loss: 0.909397\n",
      "Train Fold/Epoch: 0/2 [8000/16193 (49%)]\ttrain_loss: 0.883685\n",
      "Train Fold/Epoch: 0/2 [9600/16193 (59%)]\ttrain_loss: 0.823395\n",
      "Train Fold/Epoch: 0/2 [11200/16193 (69%)]\ttrain_loss: 1.195226\n",
      "Train Fold/Epoch: 0/2 [12800/16193 (79%)]\ttrain_loss: 0.736969\n",
      "Train Fold/Epoch: 0/2 [14400/16193 (89%)]\ttrain_loss: 0.817231\n",
      "Train Fold/Epoch: 0/2 [16000/16193 (99%)]\ttrain_loss: 1.189925\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0001, Accuracy: 8064/16193 (49.79930%)\n",
      "Test set for fold 0:  Average test_loss:  0.9450, Accuracy: 2046/4049 (50.53100%)\n",
      "\n",
      "Train Fold/Epoch: 0/3 [0/16193 (0%)]\ttrain_loss: 0.839725\n",
      "Train Fold/Epoch: 0/3 [1600/16193 (10%)]\ttrain_loss: 1.153258\n",
      "Train Fold/Epoch: 0/3 [3200/16193 (20%)]\ttrain_loss: 1.010723\n",
      "Train Fold/Epoch: 0/3 [4800/16193 (30%)]\ttrain_loss: 0.839180\n",
      "Train Fold/Epoch: 0/3 [6400/16193 (39%)]\ttrain_loss: 0.903473\n",
      "Train Fold/Epoch: 0/3 [8000/16193 (49%)]\ttrain_loss: 0.891306\n",
      "Train Fold/Epoch: 0/3 [9600/16193 (59%)]\ttrain_loss: 0.824439\n",
      "Train Fold/Epoch: 0/3 [11200/16193 (69%)]\ttrain_loss: 1.192802\n",
      "Train Fold/Epoch: 0/3 [12800/16193 (79%)]\ttrain_loss: 0.728911\n",
      "Train Fold/Epoch: 0/3 [14400/16193 (89%)]\ttrain_loss: 0.808334\n",
      "Train Fold/Epoch: 0/3 [16000/16193 (99%)]\ttrain_loss: 1.191209\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0001, Accuracy: 8108/16193 (50.07102%)\n",
      "Test set for fold 0:  Average test_loss:  0.9429, Accuracy: 2064/4049 (50.97555%)\n",
      "\n",
      "Train Fold/Epoch: 0/4 [0/16193 (0%)]\ttrain_loss: 0.833805\n",
      "Train Fold/Epoch: 0/4 [1600/16193 (10%)]\ttrain_loss: 1.143407\n",
      "Train Fold/Epoch: 0/4 [3200/16193 (20%)]\ttrain_loss: 1.018101\n",
      "Train Fold/Epoch: 0/4 [4800/16193 (30%)]\ttrain_loss: 0.829439\n",
      "Train Fold/Epoch: 0/4 [6400/16193 (39%)]\ttrain_loss: 0.898324\n",
      "Train Fold/Epoch: 0/4 [8000/16193 (49%)]\ttrain_loss: 0.892805\n",
      "Train Fold/Epoch: 0/4 [9600/16193 (59%)]\ttrain_loss: 0.827877\n",
      "Train Fold/Epoch: 0/4 [11200/16193 (69%)]\ttrain_loss: 1.194863\n",
      "Train Fold/Epoch: 0/4 [12800/16193 (79%)]\ttrain_loss: 0.720440\n",
      "Train Fold/Epoch: 0/4 [14400/16193 (89%)]\ttrain_loss: 0.802295\n",
      "Train Fold/Epoch: 0/4 [16000/16193 (99%)]\ttrain_loss: 1.192882\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0001, Accuracy: 8157/16193 (50.37362%)\n",
      "Test set for fold 0:  Average test_loss:  0.9409, Accuracy: 2080/4049 (51.37071%)\n",
      "\n",
      "Train Fold/Epoch: 0/5 [0/16193 (0%)]\ttrain_loss: 0.827852\n",
      "Train Fold/Epoch: 0/5 [1600/16193 (10%)]\ttrain_loss: 1.129651\n",
      "Train Fold/Epoch: 0/5 [3200/16193 (20%)]\ttrain_loss: 1.028070\n",
      "Train Fold/Epoch: 0/5 [4800/16193 (30%)]\ttrain_loss: 0.820257\n",
      "Train Fold/Epoch: 0/5 [6400/16193 (39%)]\ttrain_loss: 0.894210\n",
      "Train Fold/Epoch: 0/5 [8000/16193 (49%)]\ttrain_loss: 0.896095\n",
      "Train Fold/Epoch: 0/5 [9600/16193 (59%)]\ttrain_loss: 0.831203\n",
      "Train Fold/Epoch: 0/5 [11200/16193 (69%)]\ttrain_loss: 1.195251\n",
      "Train Fold/Epoch: 0/5 [12800/16193 (79%)]\ttrain_loss: 0.715174\n",
      "Train Fold/Epoch: 0/5 [14400/16193 (89%)]\ttrain_loss: 0.796820\n",
      "Train Fold/Epoch: 0/5 [16000/16193 (99%)]\ttrain_loss: 1.195017\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0001, Accuracy: 8195/16193 (50.60829%)\n",
      "Test set for fold 0:  Average test_loss:  0.9384, Accuracy: 2082/4049 (51.42010%)\n",
      "\n",
      "Train Fold/Epoch: 0/6 [0/16193 (0%)]\ttrain_loss: 0.821233\n",
      "Train Fold/Epoch: 0/6 [1600/16193 (10%)]\ttrain_loss: 1.120044\n",
      "Train Fold/Epoch: 0/6 [3200/16193 (20%)]\ttrain_loss: 1.033033\n",
      "Train Fold/Epoch: 0/6 [4800/16193 (30%)]\ttrain_loss: 0.814524\n",
      "Train Fold/Epoch: 0/6 [6400/16193 (39%)]\ttrain_loss: 0.889271\n",
      "Train Fold/Epoch: 0/6 [8000/16193 (49%)]\ttrain_loss: 0.902383\n",
      "Train Fold/Epoch: 0/6 [9600/16193 (59%)]\ttrain_loss: 0.836737\n",
      "Train Fold/Epoch: 0/6 [11200/16193 (69%)]\ttrain_loss: 1.197644\n",
      "Train Fold/Epoch: 0/6 [12800/16193 (79%)]\ttrain_loss: 0.709429\n",
      "Train Fold/Epoch: 0/6 [14400/16193 (89%)]\ttrain_loss: 0.791269\n",
      "Train Fold/Epoch: 0/6 [16000/16193 (99%)]\ttrain_loss: 1.193907\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0001, Accuracy: 8235/16193 (50.85531%)\n",
      "Test set for fold 0:  Average test_loss:  0.9361, Accuracy: 2090/4049 (51.61768%)\n",
      "\n",
      "Train Fold/Epoch: 0/7 [0/16193 (0%)]\ttrain_loss: 0.818021\n",
      "Train Fold/Epoch: 0/7 [1600/16193 (10%)]\ttrain_loss: 1.113467\n",
      "Train Fold/Epoch: 0/7 [3200/16193 (20%)]\ttrain_loss: 1.033489\n",
      "Train Fold/Epoch: 0/7 [4800/16193 (30%)]\ttrain_loss: 0.810537\n",
      "Train Fold/Epoch: 0/7 [6400/16193 (39%)]\ttrain_loss: 0.886818\n",
      "Train Fold/Epoch: 0/7 [8000/16193 (49%)]\ttrain_loss: 0.905484\n",
      "Train Fold/Epoch: 0/7 [9600/16193 (59%)]\ttrain_loss: 0.842150\n",
      "Train Fold/Epoch: 0/7 [11200/16193 (69%)]\ttrain_loss: 1.198609\n",
      "Train Fold/Epoch: 0/7 [12800/16193 (79%)]\ttrain_loss: 0.701505\n",
      "Train Fold/Epoch: 0/7 [14400/16193 (89%)]\ttrain_loss: 0.789122\n",
      "Train Fold/Epoch: 0/7 [16000/16193 (99%)]\ttrain_loss: 1.192458\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0001, Accuracy: 8274/16193 (51.09615%)\n",
      "Test set for fold 0:  Average test_loss:  0.9340, Accuracy: 2101/4049 (51.88936%)\n",
      "\n",
      "Train Fold/Epoch: 0/8 [0/16193 (0%)]\ttrain_loss: 0.814047\n",
      "Train Fold/Epoch: 0/8 [1600/16193 (10%)]\ttrain_loss: 1.109369\n",
      "Train Fold/Epoch: 0/8 [3200/16193 (20%)]\ttrain_loss: 1.035378\n",
      "Train Fold/Epoch: 0/8 [4800/16193 (30%)]\ttrain_loss: 0.807324\n",
      "Train Fold/Epoch: 0/8 [6400/16193 (39%)]\ttrain_loss: 0.881964\n",
      "Train Fold/Epoch: 0/8 [8000/16193 (49%)]\ttrain_loss: 0.910437\n",
      "Train Fold/Epoch: 0/8 [9600/16193 (59%)]\ttrain_loss: 0.848950\n",
      "Train Fold/Epoch: 0/8 [11200/16193 (69%)]\ttrain_loss: 1.200376\n",
      "Train Fold/Epoch: 0/8 [12800/16193 (79%)]\ttrain_loss: 0.694142\n",
      "Train Fold/Epoch: 0/8 [14400/16193 (89%)]\ttrain_loss: 0.785150\n",
      "Train Fold/Epoch: 0/8 [16000/16193 (99%)]\ttrain_loss: 1.187896\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0001, Accuracy: 8302/16193 (51.26907%)\n",
      "Test set for fold 0:  Average test_loss:  0.9316, Accuracy: 2107/4049 (52.03754%)\n",
      "\n",
      "Train Fold/Epoch: 0/9 [0/16193 (0%)]\ttrain_loss: 0.812989\n",
      "Train Fold/Epoch: 0/9 [1600/16193 (10%)]\ttrain_loss: 1.106912\n",
      "Train Fold/Epoch: 0/9 [3200/16193 (20%)]\ttrain_loss: 1.034775\n",
      "Train Fold/Epoch: 0/9 [4800/16193 (30%)]\ttrain_loss: 0.804870\n",
      "Train Fold/Epoch: 0/9 [6400/16193 (39%)]\ttrain_loss: 0.877371\n",
      "Train Fold/Epoch: 0/9 [8000/16193 (49%)]\ttrain_loss: 0.916000\n",
      "Train Fold/Epoch: 0/9 [9600/16193 (59%)]\ttrain_loss: 0.854561\n",
      "Train Fold/Epoch: 0/9 [11200/16193 (69%)]\ttrain_loss: 1.199760\n",
      "Train Fold/Epoch: 0/9 [12800/16193 (79%)]\ttrain_loss: 0.689233\n",
      "Train Fold/Epoch: 0/9 [14400/16193 (89%)]\ttrain_loss: 0.785166\n",
      "Train Fold/Epoch: 0/9 [16000/16193 (99%)]\ttrain_loss: 1.181983\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0001, Accuracy: 8340/16193 (51.50374%)\n",
      "Test set for fold 0:  Average test_loss:  0.9290, Accuracy: 2125/4049 (52.48209%)\n",
      "\n",
      "Train Fold/Epoch: 0/10 [0/16193 (0%)]\ttrain_loss: 0.809539\n",
      "Train Fold/Epoch: 0/10 [1600/16193 (10%)]\ttrain_loss: 1.107411\n",
      "Train Fold/Epoch: 0/10 [3200/16193 (20%)]\ttrain_loss: 1.031956\n",
      "Train Fold/Epoch: 0/10 [4800/16193 (30%)]\ttrain_loss: 0.801072\n",
      "Train Fold/Epoch: 0/10 [6400/16193 (39%)]\ttrain_loss: 0.876120\n",
      "Train Fold/Epoch: 0/10 [8000/16193 (49%)]\ttrain_loss: 0.925130\n",
      "Train Fold/Epoch: 0/10 [9600/16193 (59%)]\ttrain_loss: 0.864843\n",
      "Train Fold/Epoch: 0/10 [11200/16193 (69%)]\ttrain_loss: 1.198487\n",
      "Train Fold/Epoch: 0/10 [12800/16193 (79%)]\ttrain_loss: 0.683910\n",
      "Train Fold/Epoch: 0/10 [14400/16193 (89%)]\ttrain_loss: 0.784336\n",
      "Train Fold/Epoch: 0/10 [16000/16193 (99%)]\ttrain_loss: 1.177463\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0001, Accuracy: 8373/16193 (51.70753%)\n",
      "Test set for fold 0:  Average test_loss:  0.9267, Accuracy: 2130/4049 (52.60558%)\n",
      "\n",
      "Train Fold/Epoch: 0/11 [0/16193 (0%)]\ttrain_loss: 0.810745\n",
      "Train Fold/Epoch: 0/11 [1600/16193 (10%)]\ttrain_loss: 1.100782\n",
      "Train Fold/Epoch: 0/11 [3200/16193 (20%)]\ttrain_loss: 1.027256\n",
      "Train Fold/Epoch: 0/11 [4800/16193 (30%)]\ttrain_loss: 0.801353\n",
      "Train Fold/Epoch: 0/11 [6400/16193 (39%)]\ttrain_loss: 0.874651\n",
      "Train Fold/Epoch: 0/11 [8000/16193 (49%)]\ttrain_loss: 0.929414\n",
      "Train Fold/Epoch: 0/11 [9600/16193 (59%)]\ttrain_loss: 0.872613\n",
      "Train Fold/Epoch: 0/11 [11200/16193 (69%)]\ttrain_loss: 1.193568\n",
      "Train Fold/Epoch: 0/11 [12800/16193 (79%)]\ttrain_loss: 0.675508\n",
      "Train Fold/Epoch: 0/11 [14400/16193 (89%)]\ttrain_loss: 0.789562\n",
      "Train Fold/Epoch: 0/11 [16000/16193 (99%)]\ttrain_loss: 1.171206\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0001, Accuracy: 8374/16193 (51.71370%)\n",
      "Test set for fold 0:  Average test_loss:  0.9239, Accuracy: 2135/4049 (52.72907%)\n",
      "\n",
      "Train Fold/Epoch: 0/12 [0/16193 (0%)]\ttrain_loss: 0.806738\n",
      "Train Fold/Epoch: 0/12 [1600/16193 (10%)]\ttrain_loss: 1.100943\n",
      "Train Fold/Epoch: 0/12 [3200/16193 (20%)]\ttrain_loss: 1.022118\n",
      "Train Fold/Epoch: 0/12 [4800/16193 (30%)]\ttrain_loss: 0.800638\n",
      "Train Fold/Epoch: 0/12 [6400/16193 (39%)]\ttrain_loss: 0.871006\n",
      "Train Fold/Epoch: 0/12 [8000/16193 (49%)]\ttrain_loss: 0.935839\n",
      "Train Fold/Epoch: 0/12 [9600/16193 (59%)]\ttrain_loss: 0.881700\n",
      "Train Fold/Epoch: 0/12 [11200/16193 (69%)]\ttrain_loss: 1.195346\n",
      "Train Fold/Epoch: 0/12 [12800/16193 (79%)]\ttrain_loss: 0.670440\n",
      "Train Fold/Epoch: 0/12 [14400/16193 (89%)]\ttrain_loss: 0.792320\n",
      "Train Fold/Epoch: 0/12 [16000/16193 (99%)]\ttrain_loss: 1.165067\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0001, Accuracy: 8416/16193 (51.97307%)\n",
      "Test set for fold 0:  Average test_loss:  0.9212, Accuracy: 2132/4049 (52.65498%)\n",
      "\n",
      "Train Fold/Epoch: 0/13 [0/16193 (0%)]\ttrain_loss: 0.806079\n",
      "Train Fold/Epoch: 0/13 [1600/16193 (10%)]\ttrain_loss: 1.095276\n",
      "Train Fold/Epoch: 0/13 [3200/16193 (20%)]\ttrain_loss: 1.010043\n",
      "Train Fold/Epoch: 0/13 [4800/16193 (30%)]\ttrain_loss: 0.800952\n",
      "Train Fold/Epoch: 0/13 [6400/16193 (39%)]\ttrain_loss: 0.864003\n",
      "Train Fold/Epoch: 0/13 [8000/16193 (49%)]\ttrain_loss: 0.935391\n",
      "Train Fold/Epoch: 0/13 [9600/16193 (59%)]\ttrain_loss: 0.887772\n",
      "Train Fold/Epoch: 0/13 [11200/16193 (69%)]\ttrain_loss: 1.191448\n",
      "Train Fold/Epoch: 0/13 [12800/16193 (79%)]\ttrain_loss: 0.663781\n",
      "Train Fold/Epoch: 0/13 [14400/16193 (89%)]\ttrain_loss: 0.795543\n",
      "Train Fold/Epoch: 0/13 [16000/16193 (99%)]\ttrain_loss: 1.163899\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0001, Accuracy: 8501/16193 (52.49799%)\n",
      "Test set for fold 0:  Average test_loss:  0.9186, Accuracy: 2142/4049 (52.90195%)\n",
      "\n",
      "Train Fold/Epoch: 0/14 [0/16193 (0%)]\ttrain_loss: 0.801662\n",
      "Train Fold/Epoch: 0/14 [1600/16193 (10%)]\ttrain_loss: 1.089832\n",
      "Train Fold/Epoch: 0/14 [3200/16193 (20%)]\ttrain_loss: 0.997085\n",
      "Train Fold/Epoch: 0/14 [4800/16193 (30%)]\ttrain_loss: 0.803810\n",
      "Train Fold/Epoch: 0/14 [6400/16193 (39%)]\ttrain_loss: 0.860061\n",
      "Train Fold/Epoch: 0/14 [8000/16193 (49%)]\ttrain_loss: 0.942052\n",
      "Train Fold/Epoch: 0/14 [9600/16193 (59%)]\ttrain_loss: 0.890655\n",
      "Train Fold/Epoch: 0/14 [11200/16193 (69%)]\ttrain_loss: 1.191990\n",
      "Train Fold/Epoch: 0/14 [12800/16193 (79%)]\ttrain_loss: 0.655265\n",
      "Train Fold/Epoch: 0/14 [14400/16193 (89%)]\ttrain_loss: 0.802799\n",
      "Train Fold/Epoch: 0/14 [16000/16193 (99%)]\ttrain_loss: 1.152786\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0001, Accuracy: 8556/16193 (52.83765%)\n",
      "Test set for fold 0:  Average test_loss:  0.9163, Accuracy: 2155/4049 (53.22302%)\n",
      "\n",
      "Train Fold/Epoch: 0/15 [0/16193 (0%)]\ttrain_loss: 0.798643\n",
      "Train Fold/Epoch: 0/15 [1600/16193 (10%)]\ttrain_loss: 1.087990\n",
      "Train Fold/Epoch: 0/15 [3200/16193 (20%)]\ttrain_loss: 0.981376\n",
      "Train Fold/Epoch: 0/15 [4800/16193 (30%)]\ttrain_loss: 0.807783\n",
      "Train Fold/Epoch: 0/15 [6400/16193 (39%)]\ttrain_loss: 0.851623\n",
      "Train Fold/Epoch: 0/15 [8000/16193 (49%)]\ttrain_loss: 0.948346\n",
      "Train Fold/Epoch: 0/15 [9600/16193 (59%)]\ttrain_loss: 0.892232\n",
      "Train Fold/Epoch: 0/15 [11200/16193 (69%)]\ttrain_loss: 1.187424\n",
      "Train Fold/Epoch: 0/15 [12800/16193 (79%)]\ttrain_loss: 0.648975\n",
      "Train Fold/Epoch: 0/15 [14400/16193 (89%)]\ttrain_loss: 0.810981\n",
      "Train Fold/Epoch: 0/15 [16000/16193 (99%)]\ttrain_loss: 1.150600\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0001, Accuracy: 8605/16193 (53.14025%)\n",
      "Test set for fold 0:  Average test_loss:  0.9142, Accuracy: 2147/4049 (53.02544%)\n",
      "\n",
      "Train Fold/Epoch: 0/16 [0/16193 (0%)]\ttrain_loss: 0.795277\n",
      "Train Fold/Epoch: 0/16 [1600/16193 (10%)]\ttrain_loss: 1.086722\n",
      "Train Fold/Epoch: 0/16 [3200/16193 (20%)]\ttrain_loss: 0.962648\n",
      "Train Fold/Epoch: 0/16 [4800/16193 (30%)]\ttrain_loss: 0.809419\n",
      "Train Fold/Epoch: 0/16 [6400/16193 (39%)]\ttrain_loss: 0.837592\n",
      "Train Fold/Epoch: 0/16 [8000/16193 (49%)]\ttrain_loss: 0.947658\n",
      "Train Fold/Epoch: 0/16 [9600/16193 (59%)]\ttrain_loss: 0.891459\n",
      "Train Fold/Epoch: 0/16 [11200/16193 (69%)]\ttrain_loss: 1.185375\n",
      "Train Fold/Epoch: 0/16 [12800/16193 (79%)]\ttrain_loss: 0.642759\n",
      "Train Fold/Epoch: 0/16 [14400/16193 (89%)]\ttrain_loss: 0.825596\n",
      "Train Fold/Epoch: 0/16 [16000/16193 (99%)]\ttrain_loss: 1.141880\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0001, Accuracy: 8649/16193 (53.41197%)\n",
      "Test set for fold 0:  Average test_loss:  0.9117, Accuracy: 2156/4049 (53.24772%)\n",
      "\n",
      "Train Fold/Epoch: 0/17 [0/16193 (0%)]\ttrain_loss: 0.791176\n",
      "Train Fold/Epoch: 0/17 [1600/16193 (10%)]\ttrain_loss: 1.086775\n",
      "Train Fold/Epoch: 0/17 [3200/16193 (20%)]\ttrain_loss: 0.951177\n",
      "Train Fold/Epoch: 0/17 [4800/16193 (30%)]\ttrain_loss: 0.808993\n",
      "Train Fold/Epoch: 0/17 [6400/16193 (39%)]\ttrain_loss: 0.834126\n",
      "Train Fold/Epoch: 0/17 [8000/16193 (49%)]\ttrain_loss: 0.954352\n",
      "Train Fold/Epoch: 0/17 [9600/16193 (59%)]\ttrain_loss: 0.890991\n",
      "Train Fold/Epoch: 0/17 [11200/16193 (69%)]\ttrain_loss: 1.182177\n",
      "Train Fold/Epoch: 0/17 [12800/16193 (79%)]\ttrain_loss: 0.638929\n",
      "Train Fold/Epoch: 0/17 [14400/16193 (89%)]\ttrain_loss: 0.832997\n",
      "Train Fold/Epoch: 0/17 [16000/16193 (99%)]\ttrain_loss: 1.134052\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0001, Accuracy: 8679/16193 (53.59723%)\n",
      "Test set for fold 0:  Average test_loss:  0.9094, Accuracy: 2164/4049 (53.44530%)\n",
      "\n",
      "Train Fold/Epoch: 0/18 [0/16193 (0%)]\ttrain_loss: 0.788329\n",
      "Train Fold/Epoch: 0/18 [1600/16193 (10%)]\ttrain_loss: 1.082988\n",
      "Train Fold/Epoch: 0/18 [3200/16193 (20%)]\ttrain_loss: 0.946113\n",
      "Train Fold/Epoch: 0/18 [4800/16193 (30%)]\ttrain_loss: 0.809851\n",
      "Train Fold/Epoch: 0/18 [6400/16193 (39%)]\ttrain_loss: 0.822111\n",
      "Train Fold/Epoch: 0/18 [8000/16193 (49%)]\ttrain_loss: 0.953726\n",
      "Train Fold/Epoch: 0/18 [9600/16193 (59%)]\ttrain_loss: 0.897622\n",
      "Train Fold/Epoch: 0/18 [11200/16193 (69%)]\ttrain_loss: 1.182373\n",
      "Train Fold/Epoch: 0/18 [12800/16193 (79%)]\ttrain_loss: 0.635555\n",
      "Train Fold/Epoch: 0/18 [14400/16193 (89%)]\ttrain_loss: 0.848900\n",
      "Train Fold/Epoch: 0/18 [16000/16193 (99%)]\ttrain_loss: 1.119106\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 8712/16193 (53.80103%)\n",
      "Test set for fold 0:  Average test_loss:  0.9077, Accuracy: 2175/4049 (53.71697%)\n",
      "\n",
      "Train Fold/Epoch: 0/19 [0/16193 (0%)]\ttrain_loss: 0.791423\n",
      "Train Fold/Epoch: 0/19 [1600/16193 (10%)]\ttrain_loss: 1.088413\n",
      "Train Fold/Epoch: 0/19 [3200/16193 (20%)]\ttrain_loss: 0.931903\n",
      "Train Fold/Epoch: 0/19 [4800/16193 (30%)]\ttrain_loss: 0.806643\n",
      "Train Fold/Epoch: 0/19 [6400/16193 (39%)]\ttrain_loss: 0.812998\n",
      "Train Fold/Epoch: 0/19 [8000/16193 (49%)]\ttrain_loss: 0.953438\n",
      "Train Fold/Epoch: 0/19 [9600/16193 (59%)]\ttrain_loss: 0.895296\n",
      "Train Fold/Epoch: 0/19 [11200/16193 (69%)]\ttrain_loss: 1.179974\n",
      "Train Fold/Epoch: 0/19 [12800/16193 (79%)]\ttrain_loss: 0.632235\n",
      "Train Fold/Epoch: 0/19 [14400/16193 (89%)]\ttrain_loss: 0.845925\n",
      "Train Fold/Epoch: 0/19 [16000/16193 (99%)]\ttrain_loss: 1.113842\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 8778/16193 (54.20861%)\n",
      "Test set for fold 0:  Average test_loss:  0.9060, Accuracy: 2175/4049 (53.71697%)\n",
      "\n",
      "Train Fold/Epoch: 0/20 [0/16193 (0%)]\ttrain_loss: 0.788067\n",
      "Train Fold/Epoch: 0/20 [1600/16193 (10%)]\ttrain_loss: 1.088600\n",
      "Train Fold/Epoch: 0/20 [3200/16193 (20%)]\ttrain_loss: 0.924208\n",
      "Train Fold/Epoch: 0/20 [4800/16193 (30%)]\ttrain_loss: 0.805790\n",
      "Train Fold/Epoch: 0/20 [6400/16193 (39%)]\ttrain_loss: 0.802653\n",
      "Train Fold/Epoch: 0/20 [8000/16193 (49%)]\ttrain_loss: 0.957045\n",
      "Train Fold/Epoch: 0/20 [9600/16193 (59%)]\ttrain_loss: 0.900498\n",
      "Train Fold/Epoch: 0/20 [11200/16193 (69%)]\ttrain_loss: 1.179770\n",
      "Train Fold/Epoch: 0/20 [12800/16193 (79%)]\ttrain_loss: 0.625569\n",
      "Train Fold/Epoch: 0/20 [14400/16193 (89%)]\ttrain_loss: 0.850122\n",
      "Train Fold/Epoch: 0/20 [16000/16193 (99%)]\ttrain_loss: 1.104134\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 8849/16193 (54.64707%)\n",
      "Test set for fold 0:  Average test_loss:  0.9038, Accuracy: 2187/4049 (54.01334%)\n",
      "\n",
      "Train Fold/Epoch: 0/21 [0/16193 (0%)]\ttrain_loss: 0.787948\n",
      "Train Fold/Epoch: 0/21 [1600/16193 (10%)]\ttrain_loss: 1.083022\n",
      "Train Fold/Epoch: 0/21 [3200/16193 (20%)]\ttrain_loss: 0.917292\n",
      "Train Fold/Epoch: 0/21 [4800/16193 (30%)]\ttrain_loss: 0.801117\n",
      "Train Fold/Epoch: 0/21 [6400/16193 (39%)]\ttrain_loss: 0.793915\n",
      "Train Fold/Epoch: 0/21 [8000/16193 (49%)]\ttrain_loss: 0.958356\n",
      "Train Fold/Epoch: 0/21 [9600/16193 (59%)]\ttrain_loss: 0.899439\n",
      "Train Fold/Epoch: 0/21 [11200/16193 (69%)]\ttrain_loss: 1.183801\n",
      "Train Fold/Epoch: 0/21 [12800/16193 (79%)]\ttrain_loss: 0.618733\n",
      "Train Fold/Epoch: 0/21 [14400/16193 (89%)]\ttrain_loss: 0.858985\n",
      "Train Fold/Epoch: 0/21 [16000/16193 (99%)]\ttrain_loss: 1.088007\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 8904/16193 (54.98672%)\n",
      "Test set for fold 0:  Average test_loss:  0.9027, Accuracy: 2176/4049 (53.74166%)\n",
      "\n",
      "Train Fold/Epoch: 0/22 [0/16193 (0%)]\ttrain_loss: 0.788929\n",
      "Train Fold/Epoch: 0/22 [1600/16193 (10%)]\ttrain_loss: 1.085486\n",
      "Train Fold/Epoch: 0/22 [3200/16193 (20%)]\ttrain_loss: 0.905134\n",
      "Train Fold/Epoch: 0/22 [4800/16193 (30%)]\ttrain_loss: 0.794291\n",
      "Train Fold/Epoch: 0/22 [6400/16193 (39%)]\ttrain_loss: 0.785482\n",
      "Train Fold/Epoch: 0/22 [8000/16193 (49%)]\ttrain_loss: 0.957010\n",
      "Train Fold/Epoch: 0/22 [9600/16193 (59%)]\ttrain_loss: 0.902544\n",
      "Train Fold/Epoch: 0/22 [11200/16193 (69%)]\ttrain_loss: 1.181308\n",
      "Train Fold/Epoch: 0/22 [12800/16193 (79%)]\ttrain_loss: 0.617965\n",
      "Train Fold/Epoch: 0/22 [14400/16193 (89%)]\ttrain_loss: 0.875480\n",
      "Train Fold/Epoch: 0/22 [16000/16193 (99%)]\ttrain_loss: 1.081456\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 8934/16193 (55.17199%)\n",
      "Test set for fold 0:  Average test_loss:  0.9007, Accuracy: 2177/4049 (53.76636%)\n",
      "\n",
      "Train Fold/Epoch: 0/23 [0/16193 (0%)]\ttrain_loss: 0.791482\n",
      "Train Fold/Epoch: 0/23 [1600/16193 (10%)]\ttrain_loss: 1.083175\n",
      "Train Fold/Epoch: 0/23 [3200/16193 (20%)]\ttrain_loss: 0.904390\n",
      "Train Fold/Epoch: 0/23 [4800/16193 (30%)]\ttrain_loss: 0.786707\n",
      "Train Fold/Epoch: 0/23 [6400/16193 (39%)]\ttrain_loss: 0.778896\n",
      "Train Fold/Epoch: 0/23 [8000/16193 (49%)]\ttrain_loss: 0.960503\n",
      "Train Fold/Epoch: 0/23 [9600/16193 (59%)]\ttrain_loss: 0.903607\n",
      "Train Fold/Epoch: 0/23 [11200/16193 (69%)]\ttrain_loss: 1.178978\n",
      "Train Fold/Epoch: 0/23 [12800/16193 (79%)]\ttrain_loss: 0.609097\n",
      "Train Fold/Epoch: 0/23 [14400/16193 (89%)]\ttrain_loss: 0.878290\n",
      "Train Fold/Epoch: 0/23 [16000/16193 (99%)]\ttrain_loss: 1.079667\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 8999/16193 (55.57340%)\n",
      "Test set for fold 0:  Average test_loss:  0.8985, Accuracy: 2200/4049 (54.33440%)\n",
      "\n",
      "Train Fold/Epoch: 0/24 [0/16193 (0%)]\ttrain_loss: 0.789105\n",
      "Train Fold/Epoch: 0/24 [1600/16193 (10%)]\ttrain_loss: 1.080701\n",
      "Train Fold/Epoch: 0/24 [3200/16193 (20%)]\ttrain_loss: 0.896245\n",
      "Train Fold/Epoch: 0/24 [4800/16193 (30%)]\ttrain_loss: 0.782827\n",
      "Train Fold/Epoch: 0/24 [6400/16193 (39%)]\ttrain_loss: 0.767429\n",
      "Train Fold/Epoch: 0/24 [8000/16193 (49%)]\ttrain_loss: 0.960244\n",
      "Train Fold/Epoch: 0/24 [9600/16193 (59%)]\ttrain_loss: 0.899618\n",
      "Train Fold/Epoch: 0/24 [11200/16193 (69%)]\ttrain_loss: 1.184782\n",
      "Train Fold/Epoch: 0/24 [12800/16193 (79%)]\ttrain_loss: 0.598470\n",
      "Train Fold/Epoch: 0/24 [14400/16193 (89%)]\ttrain_loss: 0.874251\n",
      "Train Fold/Epoch: 0/24 [16000/16193 (99%)]\ttrain_loss: 1.074991\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 9043/16193 (55.84512%)\n",
      "Test set for fold 0:  Average test_loss:  0.8967, Accuracy: 2215/4049 (54.70487%)\n",
      "\n",
      "Train Fold/Epoch: 0/25 [0/16193 (0%)]\ttrain_loss: 0.785843\n",
      "Train Fold/Epoch: 0/25 [1600/16193 (10%)]\ttrain_loss: 1.080202\n",
      "Train Fold/Epoch: 0/25 [3200/16193 (20%)]\ttrain_loss: 0.892686\n",
      "Train Fold/Epoch: 0/25 [4800/16193 (30%)]\ttrain_loss: 0.777282\n",
      "Train Fold/Epoch: 0/25 [6400/16193 (39%)]\ttrain_loss: 0.761014\n",
      "Train Fold/Epoch: 0/25 [8000/16193 (49%)]\ttrain_loss: 0.964591\n",
      "Train Fold/Epoch: 0/25 [9600/16193 (59%)]\ttrain_loss: 0.902665\n",
      "Train Fold/Epoch: 0/25 [11200/16193 (69%)]\ttrain_loss: 1.193017\n",
      "Train Fold/Epoch: 0/25 [12800/16193 (79%)]\ttrain_loss: 0.593587\n",
      "Train Fold/Epoch: 0/25 [14400/16193 (89%)]\ttrain_loss: 0.898124\n",
      "Train Fold/Epoch: 0/25 [16000/16193 (99%)]\ttrain_loss: 1.075609\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 9095/16193 (56.16624%)\n",
      "Test set for fold 0:  Average test_loss:  0.8953, Accuracy: 2214/4049 (54.68017%)\n",
      "\n",
      "Train Fold/Epoch: 0/26 [0/16193 (0%)]\ttrain_loss: 0.787375\n",
      "Train Fold/Epoch: 0/26 [1600/16193 (10%)]\ttrain_loss: 1.086677\n",
      "Train Fold/Epoch: 0/26 [3200/16193 (20%)]\ttrain_loss: 0.896445\n",
      "Train Fold/Epoch: 0/26 [4800/16193 (30%)]\ttrain_loss: 0.766100\n",
      "Train Fold/Epoch: 0/26 [6400/16193 (39%)]\ttrain_loss: 0.753741\n",
      "Train Fold/Epoch: 0/26 [8000/16193 (49%)]\ttrain_loss: 0.972683\n",
      "Train Fold/Epoch: 0/26 [9600/16193 (59%)]\ttrain_loss: 0.902514\n",
      "Train Fold/Epoch: 0/26 [11200/16193 (69%)]\ttrain_loss: 1.190130\n",
      "Train Fold/Epoch: 0/26 [12800/16193 (79%)]\ttrain_loss: 0.585382\n",
      "Train Fold/Epoch: 0/26 [14400/16193 (89%)]\ttrain_loss: 0.907159\n",
      "Train Fold/Epoch: 0/26 [16000/16193 (99%)]\ttrain_loss: 1.071254\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 9129/16193 (56.37621%)\n",
      "Test set for fold 0:  Average test_loss:  0.8930, Accuracy: 2227/4049 (55.00123%)\n",
      "\n",
      "Train Fold/Epoch: 0/27 [0/16193 (0%)]\ttrain_loss: 0.791455\n",
      "Train Fold/Epoch: 0/27 [1600/16193 (10%)]\ttrain_loss: 1.073563\n",
      "Train Fold/Epoch: 0/27 [3200/16193 (20%)]\ttrain_loss: 0.889689\n",
      "Train Fold/Epoch: 0/27 [4800/16193 (30%)]\ttrain_loss: 0.765237\n",
      "Train Fold/Epoch: 0/27 [6400/16193 (39%)]\ttrain_loss: 0.748639\n",
      "Train Fold/Epoch: 0/27 [8000/16193 (49%)]\ttrain_loss: 0.975996\n",
      "Train Fold/Epoch: 0/27 [9600/16193 (59%)]\ttrain_loss: 0.903425\n",
      "Train Fold/Epoch: 0/27 [11200/16193 (69%)]\ttrain_loss: 1.195407\n",
      "Train Fold/Epoch: 0/27 [12800/16193 (79%)]\ttrain_loss: 0.579725\n",
      "Train Fold/Epoch: 0/27 [14400/16193 (89%)]\ttrain_loss: 0.906837\n",
      "Train Fold/Epoch: 0/27 [16000/16193 (99%)]\ttrain_loss: 1.070505\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 9188/16193 (56.74057%)\n",
      "Test set for fold 0:  Average test_loss:  0.8912, Accuracy: 2246/4049 (55.47049%)\n",
      "\n",
      "Train Fold/Epoch: 0/28 [0/16193 (0%)]\ttrain_loss: 0.798108\n",
      "Train Fold/Epoch: 0/28 [1600/16193 (10%)]\ttrain_loss: 1.070693\n",
      "Train Fold/Epoch: 0/28 [3200/16193 (20%)]\ttrain_loss: 0.893314\n",
      "Train Fold/Epoch: 0/28 [4800/16193 (30%)]\ttrain_loss: 0.754194\n",
      "Train Fold/Epoch: 0/28 [6400/16193 (39%)]\ttrain_loss: 0.740826\n",
      "Train Fold/Epoch: 0/28 [8000/16193 (49%)]\ttrain_loss: 0.966230\n",
      "Train Fold/Epoch: 0/28 [9600/16193 (59%)]\ttrain_loss: 0.899182\n",
      "Train Fold/Epoch: 0/28 [11200/16193 (69%)]\ttrain_loss: 1.191422\n",
      "Train Fold/Epoch: 0/28 [12800/16193 (79%)]\ttrain_loss: 0.571621\n",
      "Train Fold/Epoch: 0/28 [14400/16193 (89%)]\ttrain_loss: 0.921060\n",
      "Train Fold/Epoch: 0/28 [16000/16193 (99%)]\ttrain_loss: 1.069363\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 9236/16193 (57.03699%)\n",
      "Test set for fold 0:  Average test_loss:  0.8896, Accuracy: 2252/4049 (55.61867%)\n",
      "\n",
      "Train Fold/Epoch: 0/29 [0/16193 (0%)]\ttrain_loss: 0.802782\n",
      "Train Fold/Epoch: 0/29 [1600/16193 (10%)]\ttrain_loss: 1.061023\n",
      "Train Fold/Epoch: 0/29 [3200/16193 (20%)]\ttrain_loss: 0.895007\n",
      "Train Fold/Epoch: 0/29 [4800/16193 (30%)]\ttrain_loss: 0.748032\n",
      "Train Fold/Epoch: 0/29 [6400/16193 (39%)]\ttrain_loss: 0.734207\n",
      "Train Fold/Epoch: 0/29 [8000/16193 (49%)]\ttrain_loss: 0.964601\n",
      "Train Fold/Epoch: 0/29 [9600/16193 (59%)]\ttrain_loss: 0.900315\n",
      "Train Fold/Epoch: 0/29 [11200/16193 (69%)]\ttrain_loss: 1.192604\n",
      "Train Fold/Epoch: 0/29 [12800/16193 (79%)]\ttrain_loss: 0.559899\n",
      "Train Fold/Epoch: 0/29 [14400/16193 (89%)]\ttrain_loss: 0.920194\n",
      "Train Fold/Epoch: 0/29 [16000/16193 (99%)]\ttrain_loss: 1.070641\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 9316/16193 (57.53103%)\n",
      "Test set for fold 0:  Average test_loss:  0.8877, Accuracy: 2254/4049 (55.66807%)\n",
      "\n",
      "Train Fold/Epoch: 0/30 [0/16193 (0%)]\ttrain_loss: 0.788851\n",
      "Train Fold/Epoch: 0/30 [1600/16193 (10%)]\ttrain_loss: 1.064076\n",
      "Train Fold/Epoch: 0/30 [3200/16193 (20%)]\ttrain_loss: 0.905949\n",
      "Train Fold/Epoch: 0/30 [4800/16193 (30%)]\ttrain_loss: 0.740280\n",
      "Train Fold/Epoch: 0/30 [6400/16193 (39%)]\ttrain_loss: 0.734971\n",
      "Train Fold/Epoch: 0/30 [8000/16193 (49%)]\ttrain_loss: 0.967382\n",
      "Train Fold/Epoch: 0/30 [9600/16193 (59%)]\ttrain_loss: 0.903151\n",
      "Train Fold/Epoch: 0/30 [11200/16193 (69%)]\ttrain_loss: 1.196412\n",
      "Train Fold/Epoch: 0/30 [12800/16193 (79%)]\ttrain_loss: 0.562003\n",
      "Train Fold/Epoch: 0/30 [14400/16193 (89%)]\ttrain_loss: 0.917014\n",
      "Train Fold/Epoch: 0/30 [16000/16193 (99%)]\ttrain_loss: 1.062524\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 9336/16193 (57.65454%)\n",
      "Test set for fold 0:  Average test_loss:  0.8856, Accuracy: 2262/4049 (55.86565%)\n",
      "\n",
      "Train Fold/Epoch: 0/31 [0/16193 (0%)]\ttrain_loss: 0.779149\n",
      "Train Fold/Epoch: 0/31 [1600/16193 (10%)]\ttrain_loss: 1.059358\n",
      "Train Fold/Epoch: 0/31 [3200/16193 (20%)]\ttrain_loss: 0.916345\n",
      "Train Fold/Epoch: 0/31 [4800/16193 (30%)]\ttrain_loss: 0.733957\n",
      "Train Fold/Epoch: 0/31 [6400/16193 (39%)]\ttrain_loss: 0.729042\n",
      "Train Fold/Epoch: 0/31 [8000/16193 (49%)]\ttrain_loss: 0.968549\n",
      "Train Fold/Epoch: 0/31 [9600/16193 (59%)]\ttrain_loss: 0.896906\n",
      "Train Fold/Epoch: 0/31 [11200/16193 (69%)]\ttrain_loss: 1.202895\n",
      "Train Fold/Epoch: 0/31 [12800/16193 (79%)]\ttrain_loss: 0.549248\n",
      "Train Fold/Epoch: 0/31 [14400/16193 (89%)]\ttrain_loss: 0.912129\n",
      "Train Fold/Epoch: 0/31 [16000/16193 (99%)]\ttrain_loss: 1.065506\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 9391/16193 (57.99420%)\n",
      "Test set for fold 0:  Average test_loss:  0.8830, Accuracy: 2266/4049 (55.96444%)\n",
      "\n",
      "Train Fold/Epoch: 0/32 [0/16193 (0%)]\ttrain_loss: 0.767722\n",
      "Train Fold/Epoch: 0/32 [1600/16193 (10%)]\ttrain_loss: 1.058080\n",
      "Train Fold/Epoch: 0/32 [3200/16193 (20%)]\ttrain_loss: 0.916062\n",
      "Train Fold/Epoch: 0/32 [4800/16193 (30%)]\ttrain_loss: 0.720750\n",
      "Train Fold/Epoch: 0/32 [6400/16193 (39%)]\ttrain_loss: 0.723494\n",
      "Train Fold/Epoch: 0/32 [8000/16193 (49%)]\ttrain_loss: 0.985981\n",
      "Train Fold/Epoch: 0/32 [9600/16193 (59%)]\ttrain_loss: 0.901569\n",
      "Train Fold/Epoch: 0/32 [11200/16193 (69%)]\ttrain_loss: 1.194931\n",
      "Train Fold/Epoch: 0/32 [12800/16193 (79%)]\ttrain_loss: 0.547447\n",
      "Train Fold/Epoch: 0/32 [14400/16193 (89%)]\ttrain_loss: 0.932138\n",
      "Train Fold/Epoch: 0/32 [16000/16193 (99%)]\ttrain_loss: 1.065079\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 9436/16193 (58.27209%)\n",
      "Test set for fold 0:  Average test_loss:  0.8809, Accuracy: 2270/4049 (56.06323%)\n",
      "\n",
      "Train Fold/Epoch: 0/33 [0/16193 (0%)]\ttrain_loss: 0.764145\n",
      "Train Fold/Epoch: 0/33 [1600/16193 (10%)]\ttrain_loss: 1.048541\n",
      "Train Fold/Epoch: 0/33 [3200/16193 (20%)]\ttrain_loss: 0.918811\n",
      "Train Fold/Epoch: 0/33 [4800/16193 (30%)]\ttrain_loss: 0.714509\n",
      "Train Fold/Epoch: 0/33 [6400/16193 (39%)]\ttrain_loss: 0.713686\n",
      "Train Fold/Epoch: 0/33 [8000/16193 (49%)]\ttrain_loss: 0.979979\n",
      "Train Fold/Epoch: 0/33 [9600/16193 (59%)]\ttrain_loss: 0.903334\n",
      "Train Fold/Epoch: 0/33 [11200/16193 (69%)]\ttrain_loss: 1.214263\n",
      "Train Fold/Epoch: 0/33 [12800/16193 (79%)]\ttrain_loss: 0.537468\n",
      "Train Fold/Epoch: 0/33 [14400/16193 (89%)]\ttrain_loss: 0.929424\n",
      "Train Fold/Epoch: 0/33 [16000/16193 (99%)]\ttrain_loss: 1.065596\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 9466/16193 (58.45736%)\n",
      "Test set for fold 0:  Average test_loss:  0.8785, Accuracy: 2280/4049 (56.31020%)\n",
      "\n",
      "Train Fold/Epoch: 0/34 [0/16193 (0%)]\ttrain_loss: 0.755184\n",
      "Train Fold/Epoch: 0/34 [1600/16193 (10%)]\ttrain_loss: 1.040269\n",
      "Train Fold/Epoch: 0/34 [3200/16193 (20%)]\ttrain_loss: 0.919261\n",
      "Train Fold/Epoch: 0/34 [4800/16193 (30%)]\ttrain_loss: 0.709512\n",
      "Train Fold/Epoch: 0/34 [6400/16193 (39%)]\ttrain_loss: 0.716270\n",
      "Train Fold/Epoch: 0/34 [8000/16193 (49%)]\ttrain_loss: 0.982663\n",
      "Train Fold/Epoch: 0/34 [9600/16193 (59%)]\ttrain_loss: 0.894346\n",
      "Train Fold/Epoch: 0/34 [11200/16193 (69%)]\ttrain_loss: 1.206882\n",
      "Train Fold/Epoch: 0/34 [12800/16193 (79%)]\ttrain_loss: 0.534397\n",
      "Train Fold/Epoch: 0/34 [14400/16193 (89%)]\ttrain_loss: 0.922032\n",
      "Train Fold/Epoch: 0/34 [16000/16193 (99%)]\ttrain_loss: 1.069191\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 9514/16193 (58.75378%)\n",
      "Test set for fold 0:  Average test_loss:  0.8772, Accuracy: 2294/4049 (56.65596%)\n",
      "\n",
      "Train Fold/Epoch: 0/35 [0/16193 (0%)]\ttrain_loss: 0.751580\n",
      "Train Fold/Epoch: 0/35 [1600/16193 (10%)]\ttrain_loss: 1.022833\n",
      "Train Fold/Epoch: 0/35 [3200/16193 (20%)]\ttrain_loss: 0.930224\n",
      "Train Fold/Epoch: 0/35 [4800/16193 (30%)]\ttrain_loss: 0.702479\n",
      "Train Fold/Epoch: 0/35 [6400/16193 (39%)]\ttrain_loss: 0.716756\n",
      "Train Fold/Epoch: 0/35 [8000/16193 (49%)]\ttrain_loss: 0.991603\n",
      "Train Fold/Epoch: 0/35 [9600/16193 (59%)]\ttrain_loss: 0.896956\n",
      "Train Fold/Epoch: 0/35 [11200/16193 (69%)]\ttrain_loss: 1.203364\n",
      "Train Fold/Epoch: 0/35 [12800/16193 (79%)]\ttrain_loss: 0.528595\n",
      "Train Fold/Epoch: 0/35 [14400/16193 (89%)]\ttrain_loss: 0.926308\n",
      "Train Fold/Epoch: 0/35 [16000/16193 (99%)]\ttrain_loss: 1.070730\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 9550/16193 (58.97610%)\n",
      "Test set for fold 0:  Average test_loss:  0.8760, Accuracy: 2296/4049 (56.70536%)\n",
      "\n",
      "Train Fold/Epoch: 0/36 [0/16193 (0%)]\ttrain_loss: 0.747219\n",
      "Train Fold/Epoch: 0/36 [1600/16193 (10%)]\ttrain_loss: 1.021073\n",
      "Train Fold/Epoch: 0/36 [3200/16193 (20%)]\ttrain_loss: 0.936956\n",
      "Train Fold/Epoch: 0/36 [4800/16193 (30%)]\ttrain_loss: 0.695378\n",
      "Train Fold/Epoch: 0/36 [6400/16193 (39%)]\ttrain_loss: 0.705872\n",
      "Train Fold/Epoch: 0/36 [8000/16193 (49%)]\ttrain_loss: 1.001677\n",
      "Train Fold/Epoch: 0/36 [9600/16193 (59%)]\ttrain_loss: 0.895350\n",
      "Train Fold/Epoch: 0/36 [11200/16193 (69%)]\ttrain_loss: 1.202450\n",
      "Train Fold/Epoch: 0/36 [12800/16193 (79%)]\ttrain_loss: 0.517609\n",
      "Train Fold/Epoch: 0/36 [14400/16193 (89%)]\ttrain_loss: 0.919712\n",
      "Train Fold/Epoch: 0/36 [16000/16193 (99%)]\ttrain_loss: 1.068444\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 9628/16193 (59.45779%)\n",
      "Test set for fold 0:  Average test_loss:  0.8734, Accuracy: 2297/4049 (56.73006%)\n",
      "\n",
      "Train Fold/Epoch: 0/37 [0/16193 (0%)]\ttrain_loss: 0.732217\n",
      "Train Fold/Epoch: 0/37 [1600/16193 (10%)]\ttrain_loss: 1.013773\n",
      "Train Fold/Epoch: 0/37 [3200/16193 (20%)]\ttrain_loss: 0.946241\n",
      "Train Fold/Epoch: 0/37 [4800/16193 (30%)]\ttrain_loss: 0.688508\n",
      "Train Fold/Epoch: 0/37 [6400/16193 (39%)]\ttrain_loss: 0.705862\n",
      "Train Fold/Epoch: 0/37 [8000/16193 (49%)]\ttrain_loss: 1.005293\n",
      "Train Fold/Epoch: 0/37 [9600/16193 (59%)]\ttrain_loss: 0.894603\n",
      "Train Fold/Epoch: 0/37 [11200/16193 (69%)]\ttrain_loss: 1.197471\n",
      "Train Fold/Epoch: 0/37 [12800/16193 (79%)]\ttrain_loss: 0.512338\n",
      "Train Fold/Epoch: 0/37 [14400/16193 (89%)]\ttrain_loss: 0.909258\n",
      "Train Fold/Epoch: 0/37 [16000/16193 (99%)]\ttrain_loss: 1.061421\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 9684/16193 (59.80362%)\n",
      "Test set for fold 0:  Average test_loss:  0.8718, Accuracy: 2302/4049 (56.85354%)\n",
      "\n",
      "Train Fold/Epoch: 0/38 [0/16193 (0%)]\ttrain_loss: 0.724862\n",
      "Train Fold/Epoch: 0/38 [1600/16193 (10%)]\ttrain_loss: 1.001575\n",
      "Train Fold/Epoch: 0/38 [3200/16193 (20%)]\ttrain_loss: 0.961370\n",
      "Train Fold/Epoch: 0/38 [4800/16193 (30%)]\ttrain_loss: 0.682382\n",
      "Train Fold/Epoch: 0/38 [6400/16193 (39%)]\ttrain_loss: 0.693634\n",
      "Train Fold/Epoch: 0/38 [8000/16193 (49%)]\ttrain_loss: 1.008004\n",
      "Train Fold/Epoch: 0/38 [9600/16193 (59%)]\ttrain_loss: 0.883809\n",
      "Train Fold/Epoch: 0/38 [11200/16193 (69%)]\ttrain_loss: 1.197030\n",
      "Train Fold/Epoch: 0/38 [12800/16193 (79%)]\ttrain_loss: 0.502973\n",
      "Train Fold/Epoch: 0/38 [14400/16193 (89%)]\ttrain_loss: 0.911140\n",
      "Train Fold/Epoch: 0/38 [16000/16193 (99%)]\ttrain_loss: 1.062862\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 9710/16193 (59.96418%)\n",
      "Test set for fold 0:  Average test_loss:  0.8692, Accuracy: 2319/4049 (57.27340%)\n",
      "\n",
      "Train Fold/Epoch: 0/39 [0/16193 (0%)]\ttrain_loss: 0.728880\n",
      "Train Fold/Epoch: 0/39 [1600/16193 (10%)]\ttrain_loss: 0.993918\n",
      "Train Fold/Epoch: 0/39 [3200/16193 (20%)]\ttrain_loss: 0.961402\n",
      "Train Fold/Epoch: 0/39 [4800/16193 (30%)]\ttrain_loss: 0.679757\n",
      "Train Fold/Epoch: 0/39 [6400/16193 (39%)]\ttrain_loss: 0.690400\n",
      "Train Fold/Epoch: 0/39 [8000/16193 (49%)]\ttrain_loss: 1.003397\n",
      "Train Fold/Epoch: 0/39 [9600/16193 (59%)]\ttrain_loss: 0.894512\n",
      "Train Fold/Epoch: 0/39 [11200/16193 (69%)]\ttrain_loss: 1.194228\n",
      "Train Fold/Epoch: 0/39 [12800/16193 (79%)]\ttrain_loss: 0.499663\n",
      "Train Fold/Epoch: 0/39 [14400/16193 (89%)]\ttrain_loss: 0.913772\n",
      "Train Fold/Epoch: 0/39 [16000/16193 (99%)]\ttrain_loss: 1.054374\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 9766/16193 (60.31001%)\n",
      "Test set for fold 0:  Average test_loss:  0.8679, Accuracy: 2340/4049 (57.79205%)\n",
      "\n",
      "Train Fold/Epoch: 0/40 [0/16193 (0%)]\ttrain_loss: 0.711197\n",
      "Train Fold/Epoch: 0/40 [1600/16193 (10%)]\ttrain_loss: 0.987562\n",
      "Train Fold/Epoch: 0/40 [3200/16193 (20%)]\ttrain_loss: 0.963466\n",
      "Train Fold/Epoch: 0/40 [4800/16193 (30%)]\ttrain_loss: 0.672458\n",
      "Train Fold/Epoch: 0/40 [6400/16193 (39%)]\ttrain_loss: 0.679752\n",
      "Train Fold/Epoch: 0/40 [8000/16193 (49%)]\ttrain_loss: 1.006368\n",
      "Train Fold/Epoch: 0/40 [9600/16193 (59%)]\ttrain_loss: 0.891389\n",
      "Train Fold/Epoch: 0/40 [11200/16193 (69%)]\ttrain_loss: 1.191986\n",
      "Train Fold/Epoch: 0/40 [12800/16193 (79%)]\ttrain_loss: 0.496912\n",
      "Train Fold/Epoch: 0/40 [14400/16193 (89%)]\ttrain_loss: 0.894131\n",
      "Train Fold/Epoch: 0/40 [16000/16193 (99%)]\ttrain_loss: 1.067147\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 9821/16193 (60.64966%)\n",
      "Test set for fold 0:  Average test_loss:  0.8660, Accuracy: 2332/4049 (57.59447%)\n",
      "\n",
      "Train Fold/Epoch: 0/41 [0/16193 (0%)]\ttrain_loss: 0.713257\n",
      "Train Fold/Epoch: 0/41 [1600/16193 (10%)]\ttrain_loss: 0.983162\n",
      "Train Fold/Epoch: 0/41 [3200/16193 (20%)]\ttrain_loss: 0.970047\n",
      "Train Fold/Epoch: 0/41 [4800/16193 (30%)]\ttrain_loss: 0.665158\n",
      "Train Fold/Epoch: 0/41 [6400/16193 (39%)]\ttrain_loss: 0.674404\n",
      "Train Fold/Epoch: 0/41 [8000/16193 (49%)]\ttrain_loss: 0.996007\n",
      "Train Fold/Epoch: 0/41 [9600/16193 (59%)]\ttrain_loss: 0.879595\n",
      "Train Fold/Epoch: 0/41 [11200/16193 (69%)]\ttrain_loss: 1.179173\n",
      "Train Fold/Epoch: 0/41 [12800/16193 (79%)]\ttrain_loss: 0.492891\n",
      "Train Fold/Epoch: 0/41 [14400/16193 (89%)]\ttrain_loss: 0.885114\n",
      "Train Fold/Epoch: 0/41 [16000/16193 (99%)]\ttrain_loss: 1.051400\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 9858/16193 (60.87816%)\n",
      "Test set for fold 0:  Average test_loss:  0.8643, Accuracy: 2348/4049 (57.98963%)\n",
      "\n",
      "Train Fold/Epoch: 0/42 [0/16193 (0%)]\ttrain_loss: 0.707087\n",
      "Train Fold/Epoch: 0/42 [1600/16193 (10%)]\ttrain_loss: 0.975464\n",
      "Train Fold/Epoch: 0/42 [3200/16193 (20%)]\ttrain_loss: 0.983706\n",
      "Train Fold/Epoch: 0/42 [4800/16193 (30%)]\ttrain_loss: 0.650497\n",
      "Train Fold/Epoch: 0/42 [6400/16193 (39%)]\ttrain_loss: 0.665557\n",
      "Train Fold/Epoch: 0/42 [8000/16193 (49%)]\ttrain_loss: 1.010466\n",
      "Train Fold/Epoch: 0/42 [9600/16193 (59%)]\ttrain_loss: 0.885140\n",
      "Train Fold/Epoch: 0/42 [11200/16193 (69%)]\ttrain_loss: 1.179401\n",
      "Train Fold/Epoch: 0/42 [12800/16193 (79%)]\ttrain_loss: 0.493562\n",
      "Train Fold/Epoch: 0/42 [14400/16193 (89%)]\ttrain_loss: 0.871001\n",
      "Train Fold/Epoch: 0/42 [16000/16193 (99%)]\ttrain_loss: 1.066344\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 9894/16193 (61.10048%)\n",
      "Test set for fold 0:  Average test_loss:  0.8626, Accuracy: 2358/4049 (58.23660%)\n",
      "\n",
      "Train Fold/Epoch: 0/43 [0/16193 (0%)]\ttrain_loss: 0.687361\n",
      "Train Fold/Epoch: 0/43 [1600/16193 (10%)]\ttrain_loss: 0.959002\n",
      "Train Fold/Epoch: 0/43 [3200/16193 (20%)]\ttrain_loss: 0.994342\n",
      "Train Fold/Epoch: 0/43 [4800/16193 (30%)]\ttrain_loss: 0.645467\n",
      "Train Fold/Epoch: 0/43 [6400/16193 (39%)]\ttrain_loss: 0.659295\n",
      "Train Fold/Epoch: 0/43 [8000/16193 (49%)]\ttrain_loss: 0.994236\n",
      "Train Fold/Epoch: 0/43 [9600/16193 (59%)]\ttrain_loss: 0.881391\n",
      "Train Fold/Epoch: 0/43 [11200/16193 (69%)]\ttrain_loss: 1.175057\n",
      "Train Fold/Epoch: 0/43 [12800/16193 (79%)]\ttrain_loss: 0.482091\n",
      "Train Fold/Epoch: 0/43 [14400/16193 (89%)]\ttrain_loss: 0.856011\n",
      "Train Fold/Epoch: 0/43 [16000/16193 (99%)]\ttrain_loss: 1.060373\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 9921/16193 (61.26721%)\n",
      "Test set for fold 0:  Average test_loss:  0.8621, Accuracy: 2360/4049 (58.28600%)\n",
      "\n",
      "Train Fold/Epoch: 0/44 [0/16193 (0%)]\ttrain_loss: 0.697832\n",
      "Train Fold/Epoch: 0/44 [1600/16193 (10%)]\ttrain_loss: 0.955103\n",
      "Train Fold/Epoch: 0/44 [3200/16193 (20%)]\ttrain_loss: 0.999957\n",
      "Train Fold/Epoch: 0/44 [4800/16193 (30%)]\ttrain_loss: 0.641750\n",
      "Train Fold/Epoch: 0/44 [6400/16193 (39%)]\ttrain_loss: 0.654301\n",
      "Train Fold/Epoch: 0/44 [8000/16193 (49%)]\ttrain_loss: 1.015799\n",
      "Train Fold/Epoch: 0/44 [9600/16193 (59%)]\ttrain_loss: 0.870310\n",
      "Train Fold/Epoch: 0/44 [11200/16193 (69%)]\ttrain_loss: 1.163583\n",
      "Train Fold/Epoch: 0/44 [12800/16193 (79%)]\ttrain_loss: 0.479934\n",
      "Train Fold/Epoch: 0/44 [14400/16193 (89%)]\ttrain_loss: 0.827824\n",
      "Train Fold/Epoch: 0/44 [16000/16193 (99%)]\ttrain_loss: 1.062993\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 9975/16193 (61.60069%)\n",
      "Test set for fold 0:  Average test_loss:  0.8593, Accuracy: 2364/4049 (58.38479%)\n",
      "\n",
      "Train Fold/Epoch: 0/45 [0/16193 (0%)]\ttrain_loss: 0.687223\n",
      "Train Fold/Epoch: 0/45 [1600/16193 (10%)]\ttrain_loss: 0.939581\n",
      "Train Fold/Epoch: 0/45 [3200/16193 (20%)]\ttrain_loss: 0.993643\n",
      "Train Fold/Epoch: 0/45 [4800/16193 (30%)]\ttrain_loss: 0.633149\n",
      "Train Fold/Epoch: 0/45 [6400/16193 (39%)]\ttrain_loss: 0.646111\n",
      "Train Fold/Epoch: 0/45 [8000/16193 (49%)]\ttrain_loss: 0.972353\n",
      "Train Fold/Epoch: 0/45 [9600/16193 (59%)]\ttrain_loss: 0.871387\n",
      "Train Fold/Epoch: 0/45 [11200/16193 (69%)]\ttrain_loss: 1.158713\n",
      "Train Fold/Epoch: 0/45 [12800/16193 (79%)]\ttrain_loss: 0.473815\n",
      "Train Fold/Epoch: 0/45 [14400/16193 (89%)]\ttrain_loss: 0.824880\n",
      "Train Fold/Epoch: 0/45 [16000/16193 (99%)]\ttrain_loss: 1.058614\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 9991/16193 (61.69950%)\n",
      "Test set for fold 0:  Average test_loss:  0.8586, Accuracy: 2361/4049 (58.31069%)\n",
      "\n",
      "Train Fold/Epoch: 0/46 [0/16193 (0%)]\ttrain_loss: 0.668483\n",
      "Train Fold/Epoch: 0/46 [1600/16193 (10%)]\ttrain_loss: 0.925783\n",
      "Train Fold/Epoch: 0/46 [3200/16193 (20%)]\ttrain_loss: 1.008891\n",
      "Train Fold/Epoch: 0/46 [4800/16193 (30%)]\ttrain_loss: 0.626406\n",
      "Train Fold/Epoch: 0/46 [6400/16193 (39%)]\ttrain_loss: 0.642809\n",
      "Train Fold/Epoch: 0/46 [8000/16193 (49%)]\ttrain_loss: 0.969104\n",
      "Train Fold/Epoch: 0/46 [9600/16193 (59%)]\ttrain_loss: 0.868008\n",
      "Train Fold/Epoch: 0/46 [11200/16193 (69%)]\ttrain_loss: 1.162564\n",
      "Train Fold/Epoch: 0/46 [12800/16193 (79%)]\ttrain_loss: 0.471485\n",
      "Train Fold/Epoch: 0/46 [14400/16193 (89%)]\ttrain_loss: 0.804469\n",
      "Train Fold/Epoch: 0/46 [16000/16193 (99%)]\ttrain_loss: 1.049405\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 10020/16193 (61.87859%)\n",
      "Test set for fold 0:  Average test_loss:  0.8568, Accuracy: 2376/4049 (58.68116%)\n",
      "\n",
      "Train Fold/Epoch: 0/47 [0/16193 (0%)]\ttrain_loss: 0.663486\n",
      "Train Fold/Epoch: 0/47 [1600/16193 (10%)]\ttrain_loss: 0.916061\n",
      "Train Fold/Epoch: 0/47 [3200/16193 (20%)]\ttrain_loss: 1.022975\n",
      "Train Fold/Epoch: 0/47 [4800/16193 (30%)]\ttrain_loss: 0.611171\n",
      "Train Fold/Epoch: 0/47 [6400/16193 (39%)]\ttrain_loss: 0.638744\n",
      "Train Fold/Epoch: 0/47 [8000/16193 (49%)]\ttrain_loss: 0.942733\n",
      "Train Fold/Epoch: 0/47 [9600/16193 (59%)]\ttrain_loss: 0.859182\n",
      "Train Fold/Epoch: 0/47 [11200/16193 (69%)]\ttrain_loss: 1.171413\n",
      "Train Fold/Epoch: 0/47 [12800/16193 (79%)]\ttrain_loss: 0.464542\n",
      "Train Fold/Epoch: 0/47 [14400/16193 (89%)]\ttrain_loss: 0.797493\n",
      "Train Fold/Epoch: 0/47 [16000/16193 (99%)]\ttrain_loss: 1.062779\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 10058/16193 (62.11326%)\n",
      "Test set for fold 0:  Average test_loss:  0.8560, Accuracy: 2378/4049 (58.73055%)\n",
      "\n",
      "Train Fold/Epoch: 0/48 [0/16193 (0%)]\ttrain_loss: 0.659227\n",
      "Train Fold/Epoch: 0/48 [1600/16193 (10%)]\ttrain_loss: 0.911646\n",
      "Train Fold/Epoch: 0/48 [3200/16193 (20%)]\ttrain_loss: 1.026953\n",
      "Train Fold/Epoch: 0/48 [4800/16193 (30%)]\ttrain_loss: 0.606155\n",
      "Train Fold/Epoch: 0/48 [6400/16193 (39%)]\ttrain_loss: 0.634676\n",
      "Train Fold/Epoch: 0/48 [8000/16193 (49%)]\ttrain_loss: 0.957845\n",
      "Train Fold/Epoch: 0/48 [9600/16193 (59%)]\ttrain_loss: 0.849178\n",
      "Train Fold/Epoch: 0/48 [11200/16193 (69%)]\ttrain_loss: 1.151273\n",
      "Train Fold/Epoch: 0/48 [12800/16193 (79%)]\ttrain_loss: 0.458500\n",
      "Train Fold/Epoch: 0/48 [14400/16193 (89%)]\ttrain_loss: 0.796449\n",
      "Train Fold/Epoch: 0/48 [16000/16193 (99%)]\ttrain_loss: 1.048122\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 10125/16193 (62.52702%)\n",
      "Test set for fold 0:  Average test_loss:  0.8556, Accuracy: 2379/4049 (58.75525%)\n",
      "\n",
      "Train Fold/Epoch: 0/49 [0/16193 (0%)]\ttrain_loss: 0.639237\n",
      "Train Fold/Epoch: 0/49 [1600/16193 (10%)]\ttrain_loss: 0.904671\n",
      "Train Fold/Epoch: 0/49 [3200/16193 (20%)]\ttrain_loss: 1.029101\n",
      "Train Fold/Epoch: 0/49 [4800/16193 (30%)]\ttrain_loss: 0.596463\n",
      "Train Fold/Epoch: 0/49 [6400/16193 (39%)]\ttrain_loss: 0.628859\n",
      "Train Fold/Epoch: 0/49 [8000/16193 (49%)]\ttrain_loss: 0.931143\n",
      "Train Fold/Epoch: 0/49 [9600/16193 (59%)]\ttrain_loss: 0.838450\n",
      "Train Fold/Epoch: 0/49 [11200/16193 (69%)]\ttrain_loss: 1.144556\n",
      "Train Fold/Epoch: 0/49 [12800/16193 (79%)]\ttrain_loss: 0.453887\n",
      "Train Fold/Epoch: 0/49 [14400/16193 (89%)]\ttrain_loss: 0.798415\n",
      "Train Fold/Epoch: 0/49 [16000/16193 (99%)]\ttrain_loss: 1.042591\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 10182/16193 (62.87902%)\n",
      "Test set for fold 0:  Average test_loss:  0.8527, Accuracy: 2384/4049 (58.87874%)\n",
      "\n",
      "Train Fold/Epoch: 0/50 [0/16193 (0%)]\ttrain_loss: 0.643985\n",
      "Train Fold/Epoch: 0/50 [1600/16193 (10%)]\ttrain_loss: 0.889769\n",
      "Train Fold/Epoch: 0/50 [3200/16193 (20%)]\ttrain_loss: 1.041672\n",
      "Train Fold/Epoch: 0/50 [4800/16193 (30%)]\ttrain_loss: 0.593386\n",
      "Train Fold/Epoch: 0/50 [6400/16193 (39%)]\ttrain_loss: 0.627519\n",
      "Train Fold/Epoch: 0/50 [8000/16193 (49%)]\ttrain_loss: 1.026396\n",
      "Train Fold/Epoch: 0/50 [9600/16193 (59%)]\ttrain_loss: 0.835697\n",
      "Train Fold/Epoch: 0/50 [11200/16193 (69%)]\ttrain_loss: 1.148083\n",
      "Train Fold/Epoch: 0/50 [12800/16193 (79%)]\ttrain_loss: 0.446180\n",
      "Train Fold/Epoch: 0/50 [14400/16193 (89%)]\ttrain_loss: 0.799851\n",
      "Train Fold/Epoch: 0/50 [16000/16193 (99%)]\ttrain_loss: 1.040970\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 10218/16193 (63.10134%)\n",
      "Test set for fold 0:  Average test_loss:  0.8513, Accuracy: 2396/4049 (59.17510%)\n",
      "\n",
      "Train Fold/Epoch: 0/51 [0/16193 (0%)]\ttrain_loss: 0.638949\n",
      "Train Fold/Epoch: 0/51 [1600/16193 (10%)]\ttrain_loss: 0.872234\n",
      "Train Fold/Epoch: 0/51 [3200/16193 (20%)]\ttrain_loss: 1.050116\n",
      "Train Fold/Epoch: 0/51 [4800/16193 (30%)]\ttrain_loss: 0.586516\n",
      "Train Fold/Epoch: 0/51 [6400/16193 (39%)]\ttrain_loss: 0.617108\n",
      "Train Fold/Epoch: 0/51 [8000/16193 (49%)]\ttrain_loss: 0.951123\n",
      "Train Fold/Epoch: 0/51 [9600/16193 (59%)]\ttrain_loss: 0.826637\n",
      "Train Fold/Epoch: 0/51 [11200/16193 (69%)]\ttrain_loss: 1.137414\n",
      "Train Fold/Epoch: 0/51 [12800/16193 (79%)]\ttrain_loss: 0.441914\n",
      "Train Fold/Epoch: 0/51 [14400/16193 (89%)]\ttrain_loss: 0.785183\n",
      "Train Fold/Epoch: 0/51 [16000/16193 (99%)]\ttrain_loss: 1.043097\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 10266/16193 (63.39776%)\n",
      "Test set for fold 0:  Average test_loss:  0.8510, Accuracy: 2400/4049 (59.27389%)\n",
      "\n",
      "Train Fold/Epoch: 0/52 [0/16193 (0%)]\ttrain_loss: 0.624681\n",
      "Train Fold/Epoch: 0/52 [1600/16193 (10%)]\ttrain_loss: 0.866748\n",
      "Train Fold/Epoch: 0/52 [3200/16193 (20%)]\ttrain_loss: 1.040691\n",
      "Train Fold/Epoch: 0/52 [4800/16193 (30%)]\ttrain_loss: 0.574928\n",
      "Train Fold/Epoch: 0/52 [6400/16193 (39%)]\ttrain_loss: 0.609956\n",
      "Train Fold/Epoch: 0/52 [8000/16193 (49%)]\ttrain_loss: 0.934589\n",
      "Train Fold/Epoch: 0/52 [9600/16193 (59%)]\ttrain_loss: 0.814892\n",
      "Train Fold/Epoch: 0/52 [11200/16193 (69%)]\ttrain_loss: 1.124725\n",
      "Train Fold/Epoch: 0/52 [12800/16193 (79%)]\ttrain_loss: 0.429518\n",
      "Train Fold/Epoch: 0/52 [14400/16193 (89%)]\ttrain_loss: 0.782287\n",
      "Train Fold/Epoch: 0/52 [16000/16193 (99%)]\ttrain_loss: 1.032541\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 10328/16193 (63.78065%)\n",
      "Test set for fold 0:  Average test_loss:  0.8526, Accuracy: 2395/4049 (59.15041%)\n",
      "\n",
      "Train Fold/Epoch: 0/53 [0/16193 (0%)]\ttrain_loss: 0.624461\n",
      "Train Fold/Epoch: 0/53 [1600/16193 (10%)]\ttrain_loss: 0.859464\n",
      "Train Fold/Epoch: 0/53 [3200/16193 (20%)]\ttrain_loss: 1.035098\n",
      "Train Fold/Epoch: 0/53 [4800/16193 (30%)]\ttrain_loss: 0.567882\n",
      "Train Fold/Epoch: 0/53 [6400/16193 (39%)]\ttrain_loss: 0.609611\n",
      "Train Fold/Epoch: 0/53 [8000/16193 (49%)]\ttrain_loss: 0.929030\n",
      "Train Fold/Epoch: 0/53 [9600/16193 (59%)]\ttrain_loss: 0.812904\n",
      "Train Fold/Epoch: 0/53 [11200/16193 (69%)]\ttrain_loss: 1.118272\n",
      "Train Fold/Epoch: 0/53 [12800/16193 (79%)]\ttrain_loss: 0.429054\n",
      "Train Fold/Epoch: 0/53 [14400/16193 (89%)]\ttrain_loss: 0.787907\n",
      "Train Fold/Epoch: 0/53 [16000/16193 (99%)]\ttrain_loss: 1.022146\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 10387/16193 (64.14500%)\n",
      "Test set for fold 0:  Average test_loss:  0.8504, Accuracy: 2403/4049 (59.34799%)\n",
      "\n",
      "Train Fold/Epoch: 0/54 [0/16193 (0%)]\ttrain_loss: 0.619821\n",
      "Train Fold/Epoch: 0/54 [1600/16193 (10%)]\ttrain_loss: 0.838146\n",
      "Train Fold/Epoch: 0/54 [3200/16193 (20%)]\ttrain_loss: 1.040870\n",
      "Train Fold/Epoch: 0/54 [4800/16193 (30%)]\ttrain_loss: 0.562556\n",
      "Train Fold/Epoch: 0/54 [6400/16193 (39%)]\ttrain_loss: 0.600249\n",
      "Train Fold/Epoch: 0/54 [8000/16193 (49%)]\ttrain_loss: 0.938520\n",
      "Train Fold/Epoch: 0/54 [9600/16193 (59%)]\ttrain_loss: 0.796934\n",
      "Train Fold/Epoch: 0/54 [11200/16193 (69%)]\ttrain_loss: 1.120849\n",
      "Train Fold/Epoch: 0/54 [12800/16193 (79%)]\ttrain_loss: 0.418288\n",
      "Train Fold/Epoch: 0/54 [14400/16193 (89%)]\ttrain_loss: 0.792328\n",
      "Train Fold/Epoch: 0/54 [16000/16193 (99%)]\ttrain_loss: 1.018363\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 10405/16193 (64.25616%)\n",
      "Test set for fold 0:  Average test_loss:  0.8486, Accuracy: 2411/4049 (59.54557%)\n",
      "\n",
      "Train Fold/Epoch: 0/55 [0/16193 (0%)]\ttrain_loss: 0.614133\n",
      "Train Fold/Epoch: 0/55 [1600/16193 (10%)]\ttrain_loss: 0.835068\n",
      "Train Fold/Epoch: 0/55 [3200/16193 (20%)]\ttrain_loss: 1.047078\n",
      "Train Fold/Epoch: 0/55 [4800/16193 (30%)]\ttrain_loss: 0.545152\n",
      "Train Fold/Epoch: 0/55 [6400/16193 (39%)]\ttrain_loss: 0.599659\n",
      "Train Fold/Epoch: 0/55 [8000/16193 (49%)]\ttrain_loss: 0.949928\n",
      "Train Fold/Epoch: 0/55 [9600/16193 (59%)]\ttrain_loss: 0.791478\n",
      "Train Fold/Epoch: 0/55 [11200/16193 (69%)]\ttrain_loss: 1.113062\n",
      "Train Fold/Epoch: 0/55 [12800/16193 (79%)]\ttrain_loss: 0.417059\n",
      "Train Fold/Epoch: 0/55 [14400/16193 (89%)]\ttrain_loss: 0.788797\n",
      "Train Fold/Epoch: 0/55 [16000/16193 (99%)]\ttrain_loss: 1.015359\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 10488/16193 (64.76873%)\n",
      "Test set for fold 0:  Average test_loss:  0.8476, Accuracy: 2429/4049 (59.99012%)\n",
      "\n",
      "Train Fold/Epoch: 0/56 [0/16193 (0%)]\ttrain_loss: 0.609371\n",
      "Train Fold/Epoch: 0/56 [1600/16193 (10%)]\ttrain_loss: 0.835334\n",
      "Train Fold/Epoch: 0/56 [3200/16193 (20%)]\ttrain_loss: 1.059956\n",
      "Train Fold/Epoch: 0/56 [4800/16193 (30%)]\ttrain_loss: 0.538800\n",
      "Train Fold/Epoch: 0/56 [6400/16193 (39%)]\ttrain_loss: 0.597725\n",
      "Train Fold/Epoch: 0/56 [8000/16193 (49%)]\ttrain_loss: 0.923677\n",
      "Train Fold/Epoch: 0/56 [9600/16193 (59%)]\ttrain_loss: 0.781528\n",
      "Train Fold/Epoch: 0/56 [11200/16193 (69%)]\ttrain_loss: 1.104036\n",
      "Train Fold/Epoch: 0/56 [12800/16193 (79%)]\ttrain_loss: 0.401158\n",
      "Train Fold/Epoch: 0/56 [14400/16193 (89%)]\ttrain_loss: 0.789771\n",
      "Train Fold/Epoch: 0/56 [16000/16193 (99%)]\ttrain_loss: 1.007393\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 10526/16193 (65.00340%)\n",
      "Test set for fold 0:  Average test_loss:  0.8465, Accuracy: 2425/4049 (59.89133%)\n",
      "\n",
      "Train Fold/Epoch: 0/57 [0/16193 (0%)]\ttrain_loss: 0.600783\n",
      "Train Fold/Epoch: 0/57 [1600/16193 (10%)]\ttrain_loss: 0.824183\n",
      "Train Fold/Epoch: 0/57 [3200/16193 (20%)]\ttrain_loss: 1.059680\n",
      "Train Fold/Epoch: 0/57 [4800/16193 (30%)]\ttrain_loss: 0.539815\n",
      "Train Fold/Epoch: 0/57 [6400/16193 (39%)]\ttrain_loss: 0.596084\n",
      "Train Fold/Epoch: 0/57 [8000/16193 (49%)]\ttrain_loss: 0.916447\n",
      "Train Fold/Epoch: 0/57 [9600/16193 (59%)]\ttrain_loss: 0.769074\n",
      "Train Fold/Epoch: 0/57 [11200/16193 (69%)]\ttrain_loss: 1.109360\n",
      "Train Fold/Epoch: 0/57 [12800/16193 (79%)]\ttrain_loss: 0.400838\n",
      "Train Fold/Epoch: 0/57 [14400/16193 (89%)]\ttrain_loss: 0.782752\n",
      "Train Fold/Epoch: 0/57 [16000/16193 (99%)]\ttrain_loss: 1.011464\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 10579/16193 (65.33070%)\n",
      "Test set for fold 0:  Average test_loss:  0.8479, Accuracy: 2438/4049 (60.21240%)\n",
      "\n",
      "Train Fold/Epoch: 0/58 [0/16193 (0%)]\ttrain_loss: 0.611177\n",
      "Train Fold/Epoch: 0/58 [1600/16193 (10%)]\ttrain_loss: 0.800303\n",
      "Train Fold/Epoch: 0/58 [3200/16193 (20%)]\ttrain_loss: 1.046648\n",
      "Train Fold/Epoch: 0/58 [4800/16193 (30%)]\ttrain_loss: 0.531525\n",
      "Train Fold/Epoch: 0/58 [6400/16193 (39%)]\ttrain_loss: 0.604632\n",
      "Train Fold/Epoch: 0/58 [8000/16193 (49%)]\ttrain_loss: 0.919518\n",
      "Train Fold/Epoch: 0/58 [9600/16193 (59%)]\ttrain_loss: 0.757305\n",
      "Train Fold/Epoch: 0/58 [11200/16193 (69%)]\ttrain_loss: 1.086556\n",
      "Train Fold/Epoch: 0/58 [12800/16193 (79%)]\ttrain_loss: 0.393995\n",
      "Train Fold/Epoch: 0/58 [14400/16193 (89%)]\ttrain_loss: 0.788553\n",
      "Train Fold/Epoch: 0/58 [16000/16193 (99%)]\ttrain_loss: 1.008833\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 10646/16193 (65.74446%)\n",
      "Test set for fold 0:  Average test_loss:  0.8471, Accuracy: 2429/4049 (59.99012%)\n",
      "\n",
      "Train Fold/Epoch: 0/59 [0/16193 (0%)]\ttrain_loss: 0.600802\n",
      "Train Fold/Epoch: 0/59 [1600/16193 (10%)]\ttrain_loss: 0.808109\n",
      "Train Fold/Epoch: 0/59 [3200/16193 (20%)]\ttrain_loss: 1.049007\n",
      "Train Fold/Epoch: 0/59 [4800/16193 (30%)]\ttrain_loss: 0.519667\n",
      "Train Fold/Epoch: 0/59 [6400/16193 (39%)]\ttrain_loss: 0.600671\n",
      "Train Fold/Epoch: 0/59 [8000/16193 (49%)]\ttrain_loss: 0.909070\n",
      "Train Fold/Epoch: 0/59 [9600/16193 (59%)]\ttrain_loss: 0.758024\n",
      "Train Fold/Epoch: 0/59 [11200/16193 (69%)]\ttrain_loss: 1.084945\n",
      "Train Fold/Epoch: 0/59 [12800/16193 (79%)]\ttrain_loss: 0.385710\n",
      "Train Fold/Epoch: 0/59 [14400/16193 (89%)]\ttrain_loss: 0.776359\n",
      "Train Fold/Epoch: 0/59 [16000/16193 (99%)]\ttrain_loss: 1.013181\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 10678/16193 (65.94207%)\n",
      "Test set for fold 0:  Average test_loss:  0.8429, Accuracy: 2458/4049 (60.70635%)\n",
      "\n",
      "Train Fold/Epoch: 0/60 [0/16193 (0%)]\ttrain_loss: 0.568749\n",
      "Train Fold/Epoch: 0/60 [1600/16193 (10%)]\ttrain_loss: 0.793582\n",
      "Train Fold/Epoch: 0/60 [3200/16193 (20%)]\ttrain_loss: 1.058986\n",
      "Train Fold/Epoch: 0/60 [4800/16193 (30%)]\ttrain_loss: 0.522147\n",
      "Train Fold/Epoch: 0/60 [6400/16193 (39%)]\ttrain_loss: 0.600986\n",
      "Train Fold/Epoch: 0/60 [8000/16193 (49%)]\ttrain_loss: 0.906448\n",
      "Train Fold/Epoch: 0/60 [9600/16193 (59%)]\ttrain_loss: 0.752779\n",
      "Train Fold/Epoch: 0/60 [11200/16193 (69%)]\ttrain_loss: 1.084470\n",
      "Train Fold/Epoch: 0/60 [12800/16193 (79%)]\ttrain_loss: 0.385018\n",
      "Train Fold/Epoch: 0/60 [14400/16193 (89%)]\ttrain_loss: 0.789065\n",
      "Train Fold/Epoch: 0/60 [16000/16193 (99%)]\ttrain_loss: 1.003459\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 10712/16193 (66.15204%)\n",
      "Test set for fold 0:  Average test_loss:  0.8433, Accuracy: 2453/4049 (60.58286%)\n",
      "\n",
      "Train Fold/Epoch: 0/61 [0/16193 (0%)]\ttrain_loss: 0.561641\n",
      "Train Fold/Epoch: 0/61 [1600/16193 (10%)]\ttrain_loss: 0.791527\n",
      "Train Fold/Epoch: 0/61 [3200/16193 (20%)]\ttrain_loss: 1.072642\n",
      "Train Fold/Epoch: 0/61 [4800/16193 (30%)]\ttrain_loss: 0.509704\n",
      "Train Fold/Epoch: 0/61 [6400/16193 (39%)]\ttrain_loss: 0.593771\n",
      "Train Fold/Epoch: 0/61 [8000/16193 (49%)]\ttrain_loss: 0.905410\n",
      "Train Fold/Epoch: 0/61 [9600/16193 (59%)]\ttrain_loss: 0.753654\n",
      "Train Fold/Epoch: 0/61 [11200/16193 (69%)]\ttrain_loss: 1.068552\n",
      "Train Fold/Epoch: 0/61 [12800/16193 (79%)]\ttrain_loss: 0.377388\n",
      "Train Fold/Epoch: 0/61 [14400/16193 (89%)]\ttrain_loss: 0.783472\n",
      "Train Fold/Epoch: 0/61 [16000/16193 (99%)]\ttrain_loss: 0.999271\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 10759/16193 (66.44229%)\n",
      "Test set for fold 0:  Average test_loss:  0.8428, Accuracy: 2459/4049 (60.73104%)\n",
      "\n",
      "Train Fold/Epoch: 0/62 [0/16193 (0%)]\ttrain_loss: 0.546640\n",
      "Train Fold/Epoch: 0/62 [1600/16193 (10%)]\ttrain_loss: 0.783848\n",
      "Train Fold/Epoch: 0/62 [3200/16193 (20%)]\ttrain_loss: 1.088405\n",
      "Train Fold/Epoch: 0/62 [4800/16193 (30%)]\ttrain_loss: 0.496752\n",
      "Train Fold/Epoch: 0/62 [6400/16193 (39%)]\ttrain_loss: 0.591189\n",
      "Train Fold/Epoch: 0/62 [8000/16193 (49%)]\ttrain_loss: 0.897324\n",
      "Train Fold/Epoch: 0/62 [9600/16193 (59%)]\ttrain_loss: 0.752032\n",
      "Train Fold/Epoch: 0/62 [11200/16193 (69%)]\ttrain_loss: 1.069346\n",
      "Train Fold/Epoch: 0/62 [12800/16193 (79%)]\ttrain_loss: 0.380046\n",
      "Train Fold/Epoch: 0/62 [14400/16193 (89%)]\ttrain_loss: 0.778013\n",
      "Train Fold/Epoch: 0/62 [16000/16193 (99%)]\ttrain_loss: 0.991028\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 10805/16193 (66.72636%)\n",
      "Test set for fold 0:  Average test_loss:  0.8431, Accuracy: 2453/4049 (60.58286%)\n",
      "\n",
      "Train Fold/Epoch: 0/63 [0/16193 (0%)]\ttrain_loss: 0.541854\n",
      "Train Fold/Epoch: 0/63 [1600/16193 (10%)]\ttrain_loss: 0.771070\n",
      "Train Fold/Epoch: 0/63 [3200/16193 (20%)]\ttrain_loss: 1.064119\n",
      "Train Fold/Epoch: 0/63 [4800/16193 (30%)]\ttrain_loss: 0.485301\n",
      "Train Fold/Epoch: 0/63 [6400/16193 (39%)]\ttrain_loss: 0.577948\n",
      "Train Fold/Epoch: 0/63 [8000/16193 (49%)]\ttrain_loss: 0.886701\n",
      "Train Fold/Epoch: 0/63 [9600/16193 (59%)]\ttrain_loss: 0.734037\n",
      "Train Fold/Epoch: 0/63 [11200/16193 (69%)]\ttrain_loss: 1.057329\n",
      "Train Fold/Epoch: 0/63 [12800/16193 (79%)]\ttrain_loss: 0.374356\n",
      "Train Fold/Epoch: 0/63 [14400/16193 (89%)]\ttrain_loss: 0.777977\n",
      "Train Fold/Epoch: 0/63 [16000/16193 (99%)]\ttrain_loss: 0.996526\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 10833/16193 (66.89928%)\n",
      "Test set for fold 0:  Average test_loss:  0.8426, Accuracy: 2446/4049 (60.40998%)\n",
      "\n",
      "Train Fold/Epoch: 0/64 [0/16193 (0%)]\ttrain_loss: 0.533461\n",
      "Train Fold/Epoch: 0/64 [1600/16193 (10%)]\ttrain_loss: 0.765686\n",
      "Train Fold/Epoch: 0/64 [3200/16193 (20%)]\ttrain_loss: 1.086042\n",
      "Train Fold/Epoch: 0/64 [4800/16193 (30%)]\ttrain_loss: 0.489294\n",
      "Train Fold/Epoch: 0/64 [6400/16193 (39%)]\ttrain_loss: 0.587603\n",
      "Train Fold/Epoch: 0/64 [8000/16193 (49%)]\ttrain_loss: 0.893979\n",
      "Train Fold/Epoch: 0/64 [9600/16193 (59%)]\ttrain_loss: 0.737250\n",
      "Train Fold/Epoch: 0/64 [11200/16193 (69%)]\ttrain_loss: 1.048486\n",
      "Train Fold/Epoch: 0/64 [12800/16193 (79%)]\ttrain_loss: 0.373629\n",
      "Train Fold/Epoch: 0/64 [14400/16193 (89%)]\ttrain_loss: 0.772154\n",
      "Train Fold/Epoch: 0/64 [16000/16193 (99%)]\ttrain_loss: 0.985605\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 10853/16193 (67.02279%)\n",
      "Test set for fold 0:  Average test_loss:  0.8416, Accuracy: 2458/4049 (60.70635%)\n",
      "\n",
      "Train Fold/Epoch: 0/65 [0/16193 (0%)]\ttrain_loss: 0.518356\n",
      "Train Fold/Epoch: 0/65 [1600/16193 (10%)]\ttrain_loss: 0.750693\n",
      "Train Fold/Epoch: 0/65 [3200/16193 (20%)]\ttrain_loss: 1.093631\n",
      "Train Fold/Epoch: 0/65 [4800/16193 (30%)]\ttrain_loss: 0.477667\n",
      "Train Fold/Epoch: 0/65 [6400/16193 (39%)]\ttrain_loss: 0.585551\n",
      "Train Fold/Epoch: 0/65 [8000/16193 (49%)]\ttrain_loss: 0.893045\n",
      "Train Fold/Epoch: 0/65 [9600/16193 (59%)]\ttrain_loss: 0.734806\n",
      "Train Fold/Epoch: 0/65 [11200/16193 (69%)]\ttrain_loss: 1.042938\n",
      "Train Fold/Epoch: 0/65 [12800/16193 (79%)]\ttrain_loss: 0.374890\n",
      "Train Fold/Epoch: 0/65 [14400/16193 (89%)]\ttrain_loss: 0.780006\n",
      "Train Fold/Epoch: 0/65 [16000/16193 (99%)]\ttrain_loss: 1.006574\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 10920/16193 (67.43655%)\n",
      "Test set for fold 0:  Average test_loss:  0.8387, Accuracy: 2463/4049 (60.82983%)\n",
      "\n",
      "Train Fold/Epoch: 0/66 [0/16193 (0%)]\ttrain_loss: 0.514007\n",
      "Train Fold/Epoch: 0/66 [1600/16193 (10%)]\ttrain_loss: 0.747190\n",
      "Train Fold/Epoch: 0/66 [3200/16193 (20%)]\ttrain_loss: 1.100084\n",
      "Train Fold/Epoch: 0/66 [4800/16193 (30%)]\ttrain_loss: 0.470379\n",
      "Train Fold/Epoch: 0/66 [6400/16193 (39%)]\ttrain_loss: 0.575629\n",
      "Train Fold/Epoch: 0/66 [8000/16193 (49%)]\ttrain_loss: 0.879718\n",
      "Train Fold/Epoch: 0/66 [9600/16193 (59%)]\ttrain_loss: 0.712293\n",
      "Train Fold/Epoch: 0/66 [11200/16193 (69%)]\ttrain_loss: 1.034969\n",
      "Train Fold/Epoch: 0/66 [12800/16193 (79%)]\ttrain_loss: 0.363010\n",
      "Train Fold/Epoch: 0/66 [14400/16193 (89%)]\ttrain_loss: 0.784262\n",
      "Train Fold/Epoch: 0/66 [16000/16193 (99%)]\ttrain_loss: 0.998648\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 10959/16193 (67.67739%)\n",
      "Test set for fold 0:  Average test_loss:  0.8437, Accuracy: 2470/4049 (61.00272%)\n",
      "\n",
      "Train Fold/Epoch: 0/67 [0/16193 (0%)]\ttrain_loss: 0.507496\n",
      "Train Fold/Epoch: 0/67 [1600/16193 (10%)]\ttrain_loss: 0.736899\n",
      "Train Fold/Epoch: 0/67 [3200/16193 (20%)]\ttrain_loss: 1.091365\n",
      "Train Fold/Epoch: 0/67 [4800/16193 (30%)]\ttrain_loss: 0.456965\n",
      "Train Fold/Epoch: 0/67 [6400/16193 (39%)]\ttrain_loss: 0.573355\n",
      "Train Fold/Epoch: 0/67 [8000/16193 (49%)]\ttrain_loss: 0.887632\n",
      "Train Fold/Epoch: 0/67 [9600/16193 (59%)]\ttrain_loss: 0.714973\n",
      "Train Fold/Epoch: 0/67 [11200/16193 (69%)]\ttrain_loss: 1.010797\n",
      "Train Fold/Epoch: 0/67 [12800/16193 (79%)]\ttrain_loss: 0.354050\n",
      "Train Fold/Epoch: 0/67 [14400/16193 (89%)]\ttrain_loss: 0.783575\n",
      "Train Fold/Epoch: 0/67 [16000/16193 (99%)]\ttrain_loss: 1.012062\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 10980/16193 (67.80708%)\n",
      "Test set for fold 0:  Average test_loss:  0.8433, Accuracy: 2476/4049 (61.15090%)\n",
      "\n",
      "Train Fold/Epoch: 0/68 [0/16193 (0%)]\ttrain_loss: 0.499624\n",
      "Train Fold/Epoch: 0/68 [1600/16193 (10%)]\ttrain_loss: 0.717990\n",
      "Train Fold/Epoch: 0/68 [3200/16193 (20%)]\ttrain_loss: 1.113345\n",
      "Train Fold/Epoch: 0/68 [4800/16193 (30%)]\ttrain_loss: 0.456905\n",
      "Train Fold/Epoch: 0/68 [6400/16193 (39%)]\ttrain_loss: 0.581247\n",
      "Train Fold/Epoch: 0/68 [8000/16193 (49%)]\ttrain_loss: 0.879849\n",
      "Train Fold/Epoch: 0/68 [9600/16193 (59%)]\ttrain_loss: 0.709277\n",
      "Train Fold/Epoch: 0/68 [11200/16193 (69%)]\ttrain_loss: 1.017805\n",
      "Train Fold/Epoch: 0/68 [12800/16193 (79%)]\ttrain_loss: 0.354543\n",
      "Train Fold/Epoch: 0/68 [14400/16193 (89%)]\ttrain_loss: 0.796490\n",
      "Train Fold/Epoch: 0/68 [16000/16193 (99%)]\ttrain_loss: 1.016861\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11034/16193 (68.14055%)\n",
      "Test set for fold 0:  Average test_loss:  0.8404, Accuracy: 2478/4049 (61.20030%)\n",
      "\n",
      "Train Fold/Epoch: 0/69 [0/16193 (0%)]\ttrain_loss: 0.499022\n",
      "Train Fold/Epoch: 0/69 [1600/16193 (10%)]\ttrain_loss: 0.709021\n",
      "Train Fold/Epoch: 0/69 [3200/16193 (20%)]\ttrain_loss: 1.130280\n",
      "Train Fold/Epoch: 0/69 [4800/16193 (30%)]\ttrain_loss: 0.440449\n",
      "Train Fold/Epoch: 0/69 [6400/16193 (39%)]\ttrain_loss: 0.574911\n",
      "Train Fold/Epoch: 0/69 [8000/16193 (49%)]\ttrain_loss: 0.873141\n",
      "Train Fold/Epoch: 0/69 [9600/16193 (59%)]\ttrain_loss: 0.675818\n",
      "Train Fold/Epoch: 0/69 [11200/16193 (69%)]\ttrain_loss: 1.007817\n",
      "Train Fold/Epoch: 0/69 [12800/16193 (79%)]\ttrain_loss: 0.346551\n",
      "Train Fold/Epoch: 0/69 [14400/16193 (89%)]\ttrain_loss: 0.781204\n",
      "Train Fold/Epoch: 0/69 [16000/16193 (99%)]\ttrain_loss: 1.008467\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11078/16193 (68.41228%)\n",
      "Test set for fold 0:  Average test_loss:  0.8439, Accuracy: 2484/4049 (61.34848%)\n",
      "\n",
      "Train Fold/Epoch: 0/70 [0/16193 (0%)]\ttrain_loss: 0.497074\n",
      "Train Fold/Epoch: 0/70 [1600/16193 (10%)]\ttrain_loss: 0.717913\n",
      "Train Fold/Epoch: 0/70 [3200/16193 (20%)]\ttrain_loss: 1.109396\n",
      "Train Fold/Epoch: 0/70 [4800/16193 (30%)]\ttrain_loss: 0.436096\n",
      "Train Fold/Epoch: 0/70 [6400/16193 (39%)]\ttrain_loss: 0.583076\n",
      "Train Fold/Epoch: 0/70 [8000/16193 (49%)]\ttrain_loss: 0.860036\n",
      "Train Fold/Epoch: 0/70 [9600/16193 (59%)]\ttrain_loss: 0.683511\n",
      "Train Fold/Epoch: 0/70 [11200/16193 (69%)]\ttrain_loss: 0.999909\n",
      "Train Fold/Epoch: 0/70 [12800/16193 (79%)]\ttrain_loss: 0.350977\n",
      "Train Fold/Epoch: 0/70 [14400/16193 (89%)]\ttrain_loss: 0.791470\n",
      "Train Fold/Epoch: 0/70 [16000/16193 (99%)]\ttrain_loss: 1.010000\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11109/16193 (68.60372%)\n",
      "Test set for fold 0:  Average test_loss:  0.8429, Accuracy: 2482/4049 (61.29909%)\n",
      "\n",
      "Train Fold/Epoch: 0/71 [0/16193 (0%)]\ttrain_loss: 0.476274\n",
      "Train Fold/Epoch: 0/71 [1600/16193 (10%)]\ttrain_loss: 0.691919\n",
      "Train Fold/Epoch: 0/71 [3200/16193 (20%)]\ttrain_loss: 1.117152\n",
      "Train Fold/Epoch: 0/71 [4800/16193 (30%)]\ttrain_loss: 0.436776\n",
      "Train Fold/Epoch: 0/71 [6400/16193 (39%)]\ttrain_loss: 0.581775\n",
      "Train Fold/Epoch: 0/71 [8000/16193 (49%)]\ttrain_loss: 0.858286\n",
      "Train Fold/Epoch: 0/71 [9600/16193 (59%)]\ttrain_loss: 0.664250\n",
      "Train Fold/Epoch: 0/71 [11200/16193 (69%)]\ttrain_loss: 0.999223\n",
      "Train Fold/Epoch: 0/71 [12800/16193 (79%)]\ttrain_loss: 0.339197\n",
      "Train Fold/Epoch: 0/71 [14400/16193 (89%)]\ttrain_loss: 0.794968\n",
      "Train Fold/Epoch: 0/71 [16000/16193 (99%)]\ttrain_loss: 0.997287\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11144/16193 (68.81986%)\n",
      "Test set for fold 0:  Average test_loss:  0.8404, Accuracy: 2496/4049 (61.64485%)\n",
      "\n",
      "Train Fold/Epoch: 0/72 [0/16193 (0%)]\ttrain_loss: 0.468875\n",
      "Train Fold/Epoch: 0/72 [1600/16193 (10%)]\ttrain_loss: 0.695749\n",
      "Train Fold/Epoch: 0/72 [3200/16193 (20%)]\ttrain_loss: 1.112433\n",
      "Train Fold/Epoch: 0/72 [4800/16193 (30%)]\ttrain_loss: 0.429675\n",
      "Train Fold/Epoch: 0/72 [6400/16193 (39%)]\ttrain_loss: 0.586985\n",
      "Train Fold/Epoch: 0/72 [8000/16193 (49%)]\ttrain_loss: 1.314546\n",
      "Train Fold/Epoch: 0/72 [9600/16193 (59%)]\ttrain_loss: 0.680096\n",
      "Train Fold/Epoch: 0/72 [11200/16193 (69%)]\ttrain_loss: 0.977252\n",
      "Train Fold/Epoch: 0/72 [12800/16193 (79%)]\ttrain_loss: 0.338529\n",
      "Train Fold/Epoch: 0/72 [14400/16193 (89%)]\ttrain_loss: 0.811992\n",
      "Train Fold/Epoch: 0/72 [16000/16193 (99%)]\ttrain_loss: 1.001256\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11131/16193 (68.73958%)\n",
      "Test set for fold 0:  Average test_loss:  0.8384, Accuracy: 2524/4049 (62.33638%)\n",
      "\n",
      "Train Fold/Epoch: 0/73 [0/16193 (0%)]\ttrain_loss: 0.481497\n",
      "Train Fold/Epoch: 0/73 [1600/16193 (10%)]\ttrain_loss: 0.689595\n",
      "Train Fold/Epoch: 0/73 [3200/16193 (20%)]\ttrain_loss: 1.112327\n",
      "Train Fold/Epoch: 0/73 [4800/16193 (30%)]\ttrain_loss: 0.427388\n",
      "Train Fold/Epoch: 0/73 [6400/16193 (39%)]\ttrain_loss: 0.571205\n",
      "Train Fold/Epoch: 0/73 [8000/16193 (49%)]\ttrain_loss: 0.851030\n",
      "Train Fold/Epoch: 0/73 [9600/16193 (59%)]\ttrain_loss: 0.639561\n",
      "Train Fold/Epoch: 0/73 [11200/16193 (69%)]\ttrain_loss: 0.967782\n",
      "Train Fold/Epoch: 0/73 [12800/16193 (79%)]\ttrain_loss: 0.338665\n",
      "Train Fold/Epoch: 0/73 [14400/16193 (89%)]\ttrain_loss: 0.805006\n",
      "Train Fold/Epoch: 0/73 [16000/16193 (99%)]\ttrain_loss: 1.003140\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11213/16193 (69.24597%)\n",
      "Test set for fold 0:  Average test_loss:  0.8438, Accuracy: 2502/4049 (61.79304%)\n",
      "\n",
      "Train Fold/Epoch: 0/74 [0/16193 (0%)]\ttrain_loss: 0.470849\n",
      "Train Fold/Epoch: 0/74 [1600/16193 (10%)]\ttrain_loss: 0.682201\n",
      "Train Fold/Epoch: 0/74 [3200/16193 (20%)]\ttrain_loss: 1.123823\n",
      "Train Fold/Epoch: 0/74 [4800/16193 (30%)]\ttrain_loss: 0.425724\n",
      "Train Fold/Epoch: 0/74 [6400/16193 (39%)]\ttrain_loss: 0.568841\n",
      "Train Fold/Epoch: 0/74 [8000/16193 (49%)]\ttrain_loss: 0.833699\n",
      "Train Fold/Epoch: 0/74 [9600/16193 (59%)]\ttrain_loss: 0.634418\n",
      "Train Fold/Epoch: 0/74 [11200/16193 (69%)]\ttrain_loss: 0.963880\n",
      "Train Fold/Epoch: 0/74 [12800/16193 (79%)]\ttrain_loss: 0.329243\n",
      "Train Fold/Epoch: 0/74 [14400/16193 (89%)]\ttrain_loss: 0.792199\n",
      "Train Fold/Epoch: 0/74 [16000/16193 (99%)]\ttrain_loss: 1.008364\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11229/16193 (69.34478%)\n",
      "Test set for fold 0:  Average test_loss:  0.8450, Accuracy: 2501/4049 (61.76834%)\n",
      "\n",
      "Train Fold/Epoch: 0/75 [0/16193 (0%)]\ttrain_loss: 0.454522\n",
      "Train Fold/Epoch: 0/75 [1600/16193 (10%)]\ttrain_loss: 0.667885\n",
      "Train Fold/Epoch: 0/75 [3200/16193 (20%)]\ttrain_loss: 1.115787\n",
      "Train Fold/Epoch: 0/75 [4800/16193 (30%)]\ttrain_loss: 0.410015\n",
      "Train Fold/Epoch: 0/75 [6400/16193 (39%)]\ttrain_loss: 0.550368\n",
      "Train Fold/Epoch: 0/75 [8000/16193 (49%)]\ttrain_loss: 0.843057\n",
      "Train Fold/Epoch: 0/75 [9600/16193 (59%)]\ttrain_loss: 0.610941\n",
      "Train Fold/Epoch: 0/75 [11200/16193 (69%)]\ttrain_loss: 0.955004\n",
      "Train Fold/Epoch: 0/75 [12800/16193 (79%)]\ttrain_loss: 0.334498\n",
      "Train Fold/Epoch: 0/75 [14400/16193 (89%)]\ttrain_loss: 0.775662\n",
      "Train Fold/Epoch: 0/75 [16000/16193 (99%)]\ttrain_loss: 1.002376\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11259/16193 (69.53004%)\n",
      "Test set for fold 0:  Average test_loss:  0.8450, Accuracy: 2508/4049 (61.94122%)\n",
      "\n",
      "Train Fold/Epoch: 0/76 [0/16193 (0%)]\ttrain_loss: 0.473481\n",
      "Train Fold/Epoch: 0/76 [1600/16193 (10%)]\ttrain_loss: 0.669032\n",
      "Train Fold/Epoch: 0/76 [3200/16193 (20%)]\ttrain_loss: 1.105112\n",
      "Train Fold/Epoch: 0/76 [4800/16193 (30%)]\ttrain_loss: 0.407026\n",
      "Train Fold/Epoch: 0/76 [6400/16193 (39%)]\ttrain_loss: 0.592123\n",
      "Train Fold/Epoch: 0/76 [8000/16193 (49%)]\ttrain_loss: 1.005833\n",
      "Train Fold/Epoch: 0/76 [9600/16193 (59%)]\ttrain_loss: 0.679951\n",
      "Train Fold/Epoch: 0/76 [11200/16193 (69%)]\ttrain_loss: 0.956577\n",
      "Train Fold/Epoch: 0/76 [12800/16193 (79%)]\ttrain_loss: 0.322400\n",
      "Train Fold/Epoch: 0/76 [14400/16193 (89%)]\ttrain_loss: 0.771907\n",
      "Train Fold/Epoch: 0/76 [16000/16193 (99%)]\ttrain_loss: 0.996780\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11276/16193 (69.63503%)\n",
      "Test set for fold 0:  Average test_loss:  0.8407, Accuracy: 2521/4049 (62.26229%)\n",
      "\n",
      "Train Fold/Epoch: 0/77 [0/16193 (0%)]\ttrain_loss: 0.467390\n",
      "Train Fold/Epoch: 0/77 [1600/16193 (10%)]\ttrain_loss: 0.666201\n",
      "Train Fold/Epoch: 0/77 [3200/16193 (20%)]\ttrain_loss: 1.123021\n",
      "Train Fold/Epoch: 0/77 [4800/16193 (30%)]\ttrain_loss: 0.402979\n",
      "Train Fold/Epoch: 0/77 [6400/16193 (39%)]\ttrain_loss: 0.584464\n",
      "Train Fold/Epoch: 0/77 [8000/16193 (49%)]\ttrain_loss: 1.102955\n",
      "Train Fold/Epoch: 0/77 [9600/16193 (59%)]\ttrain_loss: 0.635440\n",
      "Train Fold/Epoch: 0/77 [11200/16193 (69%)]\ttrain_loss: 0.941793\n",
      "Train Fold/Epoch: 0/77 [12800/16193 (79%)]\ttrain_loss: 0.307468\n",
      "Train Fold/Epoch: 0/77 [14400/16193 (89%)]\ttrain_loss: 0.789826\n",
      "Train Fold/Epoch: 0/77 [16000/16193 (99%)]\ttrain_loss: 0.992175\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11327/16193 (69.94998%)\n",
      "Test set for fold 0:  Average test_loss:  0.8372, Accuracy: 2513/4049 (62.06471%)\n",
      "\n",
      "Train Fold/Epoch: 0/78 [0/16193 (0%)]\ttrain_loss: 0.456652\n",
      "Train Fold/Epoch: 0/78 [1600/16193 (10%)]\ttrain_loss: 0.659918\n",
      "Train Fold/Epoch: 0/78 [3200/16193 (20%)]\ttrain_loss: 1.094751\n",
      "Train Fold/Epoch: 0/78 [4800/16193 (30%)]\ttrain_loss: 0.409815\n",
      "Train Fold/Epoch: 0/78 [6400/16193 (39%)]\ttrain_loss: 0.580794\n",
      "Train Fold/Epoch: 0/78 [8000/16193 (49%)]\ttrain_loss: 0.816544\n",
      "Train Fold/Epoch: 0/78 [9600/16193 (59%)]\ttrain_loss: 0.614454\n",
      "Train Fold/Epoch: 0/78 [11200/16193 (69%)]\ttrain_loss: 0.938121\n",
      "Train Fold/Epoch: 0/78 [12800/16193 (79%)]\ttrain_loss: 0.331437\n",
      "Train Fold/Epoch: 0/78 [14400/16193 (89%)]\ttrain_loss: 0.984452\n",
      "Train Fold/Epoch: 0/78 [16000/16193 (99%)]\ttrain_loss: 0.975345\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11404/16193 (70.42549%)\n",
      "Test set for fold 0:  Average test_loss:  0.8391, Accuracy: 2527/4049 (62.41047%)\n",
      "\n",
      "Train Fold/Epoch: 0/79 [0/16193 (0%)]\ttrain_loss: 0.457136\n",
      "Train Fold/Epoch: 0/79 [1600/16193 (10%)]\ttrain_loss: 0.658542\n",
      "Train Fold/Epoch: 0/79 [3200/16193 (20%)]\ttrain_loss: 1.128574\n",
      "Train Fold/Epoch: 0/79 [4800/16193 (30%)]\ttrain_loss: 0.381828\n",
      "Train Fold/Epoch: 0/79 [6400/16193 (39%)]\ttrain_loss: 0.571809\n",
      "Train Fold/Epoch: 0/79 [8000/16193 (49%)]\ttrain_loss: 0.798268\n",
      "Train Fold/Epoch: 0/79 [9600/16193 (59%)]\ttrain_loss: 0.591327\n",
      "Train Fold/Epoch: 0/79 [11200/16193 (69%)]\ttrain_loss: 0.911606\n",
      "Train Fold/Epoch: 0/79 [12800/16193 (79%)]\ttrain_loss: 0.314165\n",
      "Train Fold/Epoch: 0/79 [14400/16193 (89%)]\ttrain_loss: 0.774646\n",
      "Train Fold/Epoch: 0/79 [16000/16193 (99%)]\ttrain_loss: 0.954644\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11417/16193 (70.50577%)\n",
      "Test set for fold 0:  Average test_loss:  0.8473, Accuracy: 2520/4049 (62.23759%)\n",
      "\n",
      "Train Fold/Epoch: 0/80 [0/16193 (0%)]\ttrain_loss: 0.434109\n",
      "Train Fold/Epoch: 0/80 [1600/16193 (10%)]\ttrain_loss: 0.642655\n",
      "Train Fold/Epoch: 0/80 [3200/16193 (20%)]\ttrain_loss: 1.100775\n",
      "Train Fold/Epoch: 0/80 [4800/16193 (30%)]\ttrain_loss: 0.396508\n",
      "Train Fold/Epoch: 0/80 [6400/16193 (39%)]\ttrain_loss: 0.593811\n",
      "Train Fold/Epoch: 0/80 [8000/16193 (49%)]\ttrain_loss: 1.124783\n",
      "Train Fold/Epoch: 0/80 [9600/16193 (59%)]\ttrain_loss: 0.636181\n",
      "Train Fold/Epoch: 0/80 [11200/16193 (69%)]\ttrain_loss: 0.917028\n",
      "Train Fold/Epoch: 0/80 [12800/16193 (79%)]\ttrain_loss: 0.322930\n",
      "Train Fold/Epoch: 0/80 [14400/16193 (89%)]\ttrain_loss: 0.762926\n",
      "Train Fold/Epoch: 0/80 [16000/16193 (99%)]\ttrain_loss: 0.941385\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11411/16193 (70.46872%)\n",
      "Test set for fold 0:  Average test_loss:  0.8444, Accuracy: 2520/4049 (62.23759%)\n",
      "\n",
      "Train Fold/Epoch: 0/81 [0/16193 (0%)]\ttrain_loss: 0.445480\n",
      "Train Fold/Epoch: 0/81 [1600/16193 (10%)]\ttrain_loss: 0.631417\n",
      "Train Fold/Epoch: 0/81 [3200/16193 (20%)]\ttrain_loss: 1.085756\n",
      "Train Fold/Epoch: 0/81 [4800/16193 (30%)]\ttrain_loss: 0.401119\n",
      "Train Fold/Epoch: 0/81 [6400/16193 (39%)]\ttrain_loss: 0.582562\n",
      "Train Fold/Epoch: 0/81 [8000/16193 (49%)]\ttrain_loss: 0.819511\n",
      "Train Fold/Epoch: 0/81 [9600/16193 (59%)]\ttrain_loss: 0.577611\n",
      "Train Fold/Epoch: 0/81 [11200/16193 (69%)]\ttrain_loss: 0.893234\n",
      "Train Fold/Epoch: 0/81 [12800/16193 (79%)]\ttrain_loss: 0.304572\n",
      "Train Fold/Epoch: 0/81 [14400/16193 (89%)]\ttrain_loss: 0.774692\n",
      "Train Fold/Epoch: 0/81 [16000/16193 (99%)]\ttrain_loss: 0.956342\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11476/16193 (70.87013%)\n",
      "Test set for fold 0:  Average test_loss:  0.8511, Accuracy: 2520/4049 (62.23759%)\n",
      "\n",
      "Train Fold/Epoch: 0/82 [0/16193 (0%)]\ttrain_loss: 0.443148\n",
      "Train Fold/Epoch: 0/82 [1600/16193 (10%)]\ttrain_loss: 0.634796\n",
      "Train Fold/Epoch: 0/82 [3200/16193 (20%)]\ttrain_loss: 1.089977\n",
      "Train Fold/Epoch: 0/82 [4800/16193 (30%)]\ttrain_loss: 0.373210\n",
      "Train Fold/Epoch: 0/82 [6400/16193 (39%)]\ttrain_loss: 0.582498\n",
      "Train Fold/Epoch: 0/82 [8000/16193 (49%)]\ttrain_loss: 0.817785\n",
      "Train Fold/Epoch: 0/82 [9600/16193 (59%)]\ttrain_loss: 0.590225\n",
      "Train Fold/Epoch: 0/82 [11200/16193 (69%)]\ttrain_loss: 0.886923\n",
      "Train Fold/Epoch: 0/82 [12800/16193 (79%)]\ttrain_loss: 0.304231\n",
      "Train Fold/Epoch: 0/82 [14400/16193 (89%)]\ttrain_loss: 0.774793\n",
      "Train Fold/Epoch: 0/82 [16000/16193 (99%)]\ttrain_loss: 0.954068\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11462/16193 (70.78367%)\n",
      "Test set for fold 0:  Average test_loss:  0.8547, Accuracy: 2514/4049 (62.08940%)\n",
      "\n",
      "Train Fold/Epoch: 0/83 [0/16193 (0%)]\ttrain_loss: 0.450276\n",
      "Train Fold/Epoch: 0/83 [1600/16193 (10%)]\ttrain_loss: 0.641079\n",
      "Train Fold/Epoch: 0/83 [3200/16193 (20%)]\ttrain_loss: 1.076141\n",
      "Train Fold/Epoch: 0/83 [4800/16193 (30%)]\ttrain_loss: 0.369535\n",
      "Train Fold/Epoch: 0/83 [6400/16193 (39%)]\ttrain_loss: 0.565266\n",
      "Train Fold/Epoch: 0/83 [8000/16193 (49%)]\ttrain_loss: 0.823839\n",
      "Train Fold/Epoch: 0/83 [9600/16193 (59%)]\ttrain_loss: 0.579482\n",
      "Train Fold/Epoch: 0/83 [11200/16193 (69%)]\ttrain_loss: 0.872901\n",
      "Train Fold/Epoch: 0/83 [12800/16193 (79%)]\ttrain_loss: 0.316076\n",
      "Train Fold/Epoch: 0/83 [14400/16193 (89%)]\ttrain_loss: 0.819497\n",
      "Train Fold/Epoch: 0/83 [16000/16193 (99%)]\ttrain_loss: 0.968420\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11505/16193 (71.04922%)\n",
      "Test set for fold 0:  Average test_loss:  0.8518, Accuracy: 2526/4049 (62.38577%)\n",
      "\n",
      "Train Fold/Epoch: 0/84 [0/16193 (0%)]\ttrain_loss: 0.418941\n",
      "Train Fold/Epoch: 0/84 [1600/16193 (10%)]\ttrain_loss: 0.627355\n",
      "Train Fold/Epoch: 0/84 [3200/16193 (20%)]\ttrain_loss: 1.076038\n",
      "Train Fold/Epoch: 0/84 [4800/16193 (30%)]\ttrain_loss: 0.357825\n",
      "Train Fold/Epoch: 0/84 [6400/16193 (39%)]\ttrain_loss: 0.546719\n",
      "Train Fold/Epoch: 0/84 [8000/16193 (49%)]\ttrain_loss: 0.812973\n",
      "Train Fold/Epoch: 0/84 [9600/16193 (59%)]\ttrain_loss: 0.555786\n",
      "Train Fold/Epoch: 0/84 [11200/16193 (69%)]\ttrain_loss: 0.892426\n",
      "Train Fold/Epoch: 0/84 [12800/16193 (79%)]\ttrain_loss: 0.311055\n",
      "Train Fold/Epoch: 0/84 [14400/16193 (89%)]\ttrain_loss: 0.799402\n",
      "Train Fold/Epoch: 0/84 [16000/16193 (99%)]\ttrain_loss: 0.948844\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11524/16193 (71.16655%)\n",
      "Test set for fold 0:  Average test_loss:  0.8560, Accuracy: 2515/4049 (62.11410%)\n",
      "\n",
      "Train Fold/Epoch: 0/85 [0/16193 (0%)]\ttrain_loss: 0.413534\n",
      "Train Fold/Epoch: 0/85 [1600/16193 (10%)]\ttrain_loss: 0.611798\n",
      "Train Fold/Epoch: 0/85 [3200/16193 (20%)]\ttrain_loss: 1.083265\n",
      "Train Fold/Epoch: 0/85 [4800/16193 (30%)]\ttrain_loss: 0.359567\n",
      "Train Fold/Epoch: 0/85 [6400/16193 (39%)]\ttrain_loss: 0.586025\n",
      "Train Fold/Epoch: 0/85 [8000/16193 (49%)]\ttrain_loss: 0.800360\n",
      "Train Fold/Epoch: 0/85 [9600/16193 (59%)]\ttrain_loss: 0.599989\n",
      "Train Fold/Epoch: 0/85 [11200/16193 (69%)]\ttrain_loss: 0.882707\n",
      "Train Fold/Epoch: 0/85 [12800/16193 (79%)]\ttrain_loss: 0.295748\n",
      "Train Fold/Epoch: 0/85 [14400/16193 (89%)]\ttrain_loss: 0.787478\n",
      "Train Fold/Epoch: 0/85 [16000/16193 (99%)]\ttrain_loss: 0.943796\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11554/16193 (71.35182%)\n",
      "Test set for fold 0:  Average test_loss:  0.8464, Accuracy: 2531/4049 (62.50926%)\n",
      "\n",
      "Train Fold/Epoch: 0/86 [0/16193 (0%)]\ttrain_loss: 0.395671\n",
      "Train Fold/Epoch: 0/86 [1600/16193 (10%)]\ttrain_loss: 0.616331\n",
      "Train Fold/Epoch: 0/86 [3200/16193 (20%)]\ttrain_loss: 1.078677\n",
      "Train Fold/Epoch: 0/86 [4800/16193 (30%)]\ttrain_loss: 0.363450\n",
      "Train Fold/Epoch: 0/86 [6400/16193 (39%)]\ttrain_loss: 0.590336\n",
      "Train Fold/Epoch: 0/86 [8000/16193 (49%)]\ttrain_loss: 0.810672\n",
      "Train Fold/Epoch: 0/86 [9600/16193 (59%)]\ttrain_loss: 0.524422\n",
      "Train Fold/Epoch: 0/86 [11200/16193 (69%)]\ttrain_loss: 0.872052\n",
      "Train Fold/Epoch: 0/86 [12800/16193 (79%)]\ttrain_loss: 0.308370\n",
      "Train Fold/Epoch: 0/86 [14400/16193 (89%)]\ttrain_loss: 1.107969\n",
      "Train Fold/Epoch: 0/86 [16000/16193 (99%)]\ttrain_loss: 0.941639\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11559/16193 (71.38270%)\n",
      "Test set for fold 0:  Average test_loss:  0.8449, Accuracy: 2544/4049 (62.83033%)\n",
      "\n",
      "Train Fold/Epoch: 0/87 [0/16193 (0%)]\ttrain_loss: 0.404277\n",
      "Train Fold/Epoch: 0/87 [1600/16193 (10%)]\ttrain_loss: 0.612669\n",
      "Train Fold/Epoch: 0/87 [3200/16193 (20%)]\ttrain_loss: 1.051881\n",
      "Train Fold/Epoch: 0/87 [4800/16193 (30%)]\ttrain_loss: 0.336249\n",
      "Train Fold/Epoch: 0/87 [6400/16193 (39%)]\ttrain_loss: 0.585488\n",
      "Train Fold/Epoch: 0/87 [8000/16193 (49%)]\ttrain_loss: 0.825117\n",
      "Train Fold/Epoch: 0/87 [9600/16193 (59%)]\ttrain_loss: 0.563362\n",
      "Train Fold/Epoch: 0/87 [11200/16193 (69%)]\ttrain_loss: 0.850507\n",
      "Train Fold/Epoch: 0/87 [12800/16193 (79%)]\ttrain_loss: 0.294534\n",
      "Train Fold/Epoch: 0/87 [14400/16193 (89%)]\ttrain_loss: 0.808279\n",
      "Train Fold/Epoch: 0/87 [16000/16193 (99%)]\ttrain_loss: 0.939080\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11603/16193 (71.65442%)\n",
      "Test set for fold 0:  Average test_loss:  0.8497, Accuracy: 2548/4049 (62.92912%)\n",
      "\n",
      "Train Fold/Epoch: 0/88 [0/16193 (0%)]\ttrain_loss: 0.404713\n",
      "Train Fold/Epoch: 0/88 [1600/16193 (10%)]\ttrain_loss: 0.620799\n",
      "Train Fold/Epoch: 0/88 [3200/16193 (20%)]\ttrain_loss: 1.048203\n",
      "Train Fold/Epoch: 0/88 [4800/16193 (30%)]\ttrain_loss: 0.337621\n",
      "Train Fold/Epoch: 0/88 [6400/16193 (39%)]\ttrain_loss: 0.536111\n",
      "Train Fold/Epoch: 0/88 [8000/16193 (49%)]\ttrain_loss: 0.798802\n",
      "Train Fold/Epoch: 0/88 [9600/16193 (59%)]\ttrain_loss: 0.542098\n",
      "Train Fold/Epoch: 0/88 [11200/16193 (69%)]\ttrain_loss: 0.852326\n",
      "Train Fold/Epoch: 0/88 [12800/16193 (79%)]\ttrain_loss: 0.302691\n",
      "Train Fold/Epoch: 0/88 [14400/16193 (89%)]\ttrain_loss: 0.826562\n",
      "Train Fold/Epoch: 0/88 [16000/16193 (99%)]\ttrain_loss: 0.937253\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11630/16193 (71.82116%)\n",
      "Test set for fold 0:  Average test_loss:  0.8572, Accuracy: 2507/4049 (61.91652%)\n",
      "\n",
      "Train Fold/Epoch: 0/89 [0/16193 (0%)]\ttrain_loss: 0.383961\n",
      "Train Fold/Epoch: 0/89 [1600/16193 (10%)]\ttrain_loss: 0.609791\n",
      "Train Fold/Epoch: 0/89 [3200/16193 (20%)]\ttrain_loss: 1.021962\n",
      "Train Fold/Epoch: 0/89 [4800/16193 (30%)]\ttrain_loss: 0.344134\n",
      "Train Fold/Epoch: 0/89 [6400/16193 (39%)]\ttrain_loss: 0.574340\n",
      "Train Fold/Epoch: 0/89 [8000/16193 (49%)]\ttrain_loss: 0.807219\n",
      "Train Fold/Epoch: 0/89 [9600/16193 (59%)]\ttrain_loss: 0.535692\n",
      "Train Fold/Epoch: 0/89 [11200/16193 (69%)]\ttrain_loss: 0.836623\n",
      "Train Fold/Epoch: 0/89 [12800/16193 (79%)]\ttrain_loss: 0.298463\n",
      "Train Fold/Epoch: 0/89 [14400/16193 (89%)]\ttrain_loss: 0.808007\n",
      "Train Fold/Epoch: 0/89 [16000/16193 (99%)]\ttrain_loss: 0.925007\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11648/16193 (71.93232%)\n",
      "Test set for fold 0:  Average test_loss:  0.8563, Accuracy: 2522/4049 (62.28698%)\n",
      "\n",
      "Train Fold/Epoch: 0/90 [0/16193 (0%)]\ttrain_loss: 0.405738\n",
      "Train Fold/Epoch: 0/90 [1600/16193 (10%)]\ttrain_loss: 0.592557\n",
      "Train Fold/Epoch: 0/90 [3200/16193 (20%)]\ttrain_loss: 1.048303\n",
      "Train Fold/Epoch: 0/90 [4800/16193 (30%)]\ttrain_loss: 0.344911\n",
      "Train Fold/Epoch: 0/90 [6400/16193 (39%)]\ttrain_loss: 0.575372\n",
      "Train Fold/Epoch: 0/90 [8000/16193 (49%)]\ttrain_loss: 0.821455\n",
      "Train Fold/Epoch: 0/90 [9600/16193 (59%)]\ttrain_loss: 0.551055\n",
      "Train Fold/Epoch: 0/90 [11200/16193 (69%)]\ttrain_loss: 0.821884\n",
      "Train Fold/Epoch: 0/90 [12800/16193 (79%)]\ttrain_loss: 0.285882\n",
      "Train Fold/Epoch: 0/90 [14400/16193 (89%)]\ttrain_loss: 0.833678\n",
      "Train Fold/Epoch: 0/90 [16000/16193 (99%)]\ttrain_loss: 0.945675\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11698/16193 (72.24109%)\n",
      "Test set for fold 0:  Average test_loss:  0.8660, Accuracy: 2527/4049 (62.41047%)\n",
      "\n",
      "Train Fold/Epoch: 0/91 [0/16193 (0%)]\ttrain_loss: 0.397122\n",
      "Train Fold/Epoch: 0/91 [1600/16193 (10%)]\ttrain_loss: 0.588458\n",
      "Train Fold/Epoch: 0/91 [3200/16193 (20%)]\ttrain_loss: 1.026368\n",
      "Train Fold/Epoch: 0/91 [4800/16193 (30%)]\ttrain_loss: 0.340370\n",
      "Train Fold/Epoch: 0/91 [6400/16193 (39%)]\ttrain_loss: 0.563419\n",
      "Train Fold/Epoch: 0/91 [8000/16193 (49%)]\ttrain_loss: 0.779681\n",
      "Train Fold/Epoch: 0/91 [9600/16193 (59%)]\ttrain_loss: 0.606532\n",
      "Train Fold/Epoch: 0/91 [11200/16193 (69%)]\ttrain_loss: 0.802677\n",
      "Train Fold/Epoch: 0/91 [12800/16193 (79%)]\ttrain_loss: 0.287328\n",
      "Train Fold/Epoch: 0/91 [14400/16193 (89%)]\ttrain_loss: 0.826395\n",
      "Train Fold/Epoch: 0/91 [16000/16193 (99%)]\ttrain_loss: 0.925730\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11694/16193 (72.21639%)\n",
      "Test set for fold 0:  Average test_loss:  0.8635, Accuracy: 2533/4049 (62.55866%)\n",
      "\n",
      "Train Fold/Epoch: 0/92 [0/16193 (0%)]\ttrain_loss: 0.390048\n",
      "Train Fold/Epoch: 0/92 [1600/16193 (10%)]\ttrain_loss: 0.605677\n",
      "Train Fold/Epoch: 0/92 [3200/16193 (20%)]\ttrain_loss: 1.040564\n",
      "Train Fold/Epoch: 0/92 [4800/16193 (30%)]\ttrain_loss: 0.325939\n",
      "Train Fold/Epoch: 0/92 [6400/16193 (39%)]\ttrain_loss: 0.522518\n",
      "Train Fold/Epoch: 0/92 [8000/16193 (49%)]\ttrain_loss: 0.790405\n",
      "Train Fold/Epoch: 0/92 [9600/16193 (59%)]\ttrain_loss: 0.540972\n",
      "Train Fold/Epoch: 0/92 [11200/16193 (69%)]\ttrain_loss: 0.803875\n",
      "Train Fold/Epoch: 0/92 [12800/16193 (79%)]\ttrain_loss: 0.289690\n",
      "Train Fold/Epoch: 0/92 [14400/16193 (89%)]\ttrain_loss: 0.847500\n",
      "Train Fold/Epoch: 0/92 [16000/16193 (99%)]\ttrain_loss: 0.953325\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11750/16193 (72.56222%)\n",
      "Test set for fold 0:  Average test_loss:  0.8607, Accuracy: 2524/4049 (62.33638%)\n",
      "\n",
      "Train Fold/Epoch: 0/93 [0/16193 (0%)]\ttrain_loss: 0.383565\n",
      "Train Fold/Epoch: 0/93 [1600/16193 (10%)]\ttrain_loss: 0.588910\n",
      "Train Fold/Epoch: 0/93 [3200/16193 (20%)]\ttrain_loss: 1.034241\n",
      "Train Fold/Epoch: 0/93 [4800/16193 (30%)]\ttrain_loss: 0.316105\n",
      "Train Fold/Epoch: 0/93 [6400/16193 (39%)]\ttrain_loss: 0.529162\n",
      "Train Fold/Epoch: 0/93 [8000/16193 (49%)]\ttrain_loss: 0.776539\n",
      "Train Fold/Epoch: 0/93 [9600/16193 (59%)]\ttrain_loss: 0.527360\n",
      "Train Fold/Epoch: 0/93 [11200/16193 (69%)]\ttrain_loss: 0.790613\n",
      "Train Fold/Epoch: 0/93 [12800/16193 (79%)]\ttrain_loss: 0.284011\n",
      "Train Fold/Epoch: 0/93 [14400/16193 (89%)]\ttrain_loss: 0.848064\n",
      "Train Fold/Epoch: 0/93 [16000/16193 (99%)]\ttrain_loss: 0.930126\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11734/16193 (72.46341%)\n",
      "Test set for fold 0:  Average test_loss:  0.8654, Accuracy: 2539/4049 (62.70684%)\n",
      "\n",
      "Train Fold/Epoch: 0/94 [0/16193 (0%)]\ttrain_loss: 0.379988\n",
      "Train Fold/Epoch: 0/94 [1600/16193 (10%)]\ttrain_loss: 0.582799\n",
      "Train Fold/Epoch: 0/94 [3200/16193 (20%)]\ttrain_loss: 1.059259\n",
      "Train Fold/Epoch: 0/94 [4800/16193 (30%)]\ttrain_loss: 0.309948\n",
      "Train Fold/Epoch: 0/94 [6400/16193 (39%)]\ttrain_loss: 0.614114\n",
      "Train Fold/Epoch: 0/94 [8000/16193 (49%)]\ttrain_loss: 1.420294\n",
      "Train Fold/Epoch: 0/94 [9600/16193 (59%)]\ttrain_loss: 0.521498\n",
      "Train Fold/Epoch: 0/94 [11200/16193 (69%)]\ttrain_loss: 0.843318\n",
      "Train Fold/Epoch: 0/94 [12800/16193 (79%)]\ttrain_loss: 0.272324\n",
      "Train Fold/Epoch: 0/94 [14400/16193 (89%)]\ttrain_loss: 0.826647\n",
      "Train Fold/Epoch: 0/94 [16000/16193 (99%)]\ttrain_loss: 0.918643\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11723/16193 (72.39548%)\n",
      "Test set for fold 0:  Average test_loss:  0.8670, Accuracy: 2540/4049 (62.73154%)\n",
      "\n",
      "Train Fold/Epoch: 0/95 [0/16193 (0%)]\ttrain_loss: 0.385644\n",
      "Train Fold/Epoch: 0/95 [1600/16193 (10%)]\ttrain_loss: 0.590486\n",
      "Train Fold/Epoch: 0/95 [3200/16193 (20%)]\ttrain_loss: 1.044843\n",
      "Train Fold/Epoch: 0/95 [4800/16193 (30%)]\ttrain_loss: 0.322444\n",
      "Train Fold/Epoch: 0/95 [6400/16193 (39%)]\ttrain_loss: 0.536031\n",
      "Train Fold/Epoch: 0/95 [8000/16193 (49%)]\ttrain_loss: 0.765777\n",
      "Train Fold/Epoch: 0/95 [9600/16193 (59%)]\ttrain_loss: 0.535391\n",
      "Train Fold/Epoch: 0/95 [11200/16193 (69%)]\ttrain_loss: 0.846042\n",
      "Train Fold/Epoch: 0/95 [12800/16193 (79%)]\ttrain_loss: 0.271677\n",
      "Train Fold/Epoch: 0/95 [14400/16193 (89%)]\ttrain_loss: 0.794135\n",
      "Train Fold/Epoch: 0/95 [16000/16193 (99%)]\ttrain_loss: 0.900546\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11750/16193 (72.56222%)\n",
      "Test set for fold 0:  Average test_loss:  0.8619, Accuracy: 2536/4049 (62.63275%)\n",
      "\n",
      "Train Fold/Epoch: 0/96 [0/16193 (0%)]\ttrain_loss: 0.395483\n",
      "Train Fold/Epoch: 0/96 [1600/16193 (10%)]\ttrain_loss: 0.596351\n",
      "Train Fold/Epoch: 0/96 [3200/16193 (20%)]\ttrain_loss: 1.021781\n",
      "Train Fold/Epoch: 0/96 [4800/16193 (30%)]\ttrain_loss: 0.290887\n",
      "Train Fold/Epoch: 0/96 [6400/16193 (39%)]\ttrain_loss: 0.544107\n",
      "Train Fold/Epoch: 0/96 [8000/16193 (49%)]\ttrain_loss: 0.756914\n",
      "Train Fold/Epoch: 0/96 [9600/16193 (59%)]\ttrain_loss: 0.499763\n",
      "Train Fold/Epoch: 0/96 [11200/16193 (69%)]\ttrain_loss: 0.798974\n",
      "Train Fold/Epoch: 0/96 [12800/16193 (79%)]\ttrain_loss: 0.268476\n",
      "Train Fold/Epoch: 0/96 [14400/16193 (89%)]\ttrain_loss: 0.839338\n",
      "Train Fold/Epoch: 0/96 [16000/16193 (99%)]\ttrain_loss: 0.924186\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11841/16193 (73.12419%)\n",
      "Test set for fold 0:  Average test_loss:  0.8704, Accuracy: 2536/4049 (62.63275%)\n",
      "\n",
      "Train Fold/Epoch: 0/97 [0/16193 (0%)]\ttrain_loss: 0.375987\n",
      "Train Fold/Epoch: 0/97 [1600/16193 (10%)]\ttrain_loss: 0.595522\n",
      "Train Fold/Epoch: 0/97 [3200/16193 (20%)]\ttrain_loss: 1.028247\n",
      "Train Fold/Epoch: 0/97 [4800/16193 (30%)]\ttrain_loss: 0.313931\n",
      "Train Fold/Epoch: 0/97 [6400/16193 (39%)]\ttrain_loss: 0.534024\n",
      "Train Fold/Epoch: 0/97 [8000/16193 (49%)]\ttrain_loss: 0.767977\n",
      "Train Fold/Epoch: 0/97 [9600/16193 (59%)]\ttrain_loss: 0.485436\n",
      "Train Fold/Epoch: 0/97 [11200/16193 (69%)]\ttrain_loss: 0.801125\n",
      "Train Fold/Epoch: 0/97 [12800/16193 (79%)]\ttrain_loss: 0.256373\n",
      "Train Fold/Epoch: 0/97 [14400/16193 (89%)]\ttrain_loss: 0.814721\n",
      "Train Fold/Epoch: 0/97 [16000/16193 (99%)]\ttrain_loss: 0.930985\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11835/16193 (73.08714%)\n",
      "Test set for fold 0:  Average test_loss:  0.8674, Accuracy: 2528/4049 (62.43517%)\n",
      "\n",
      "Train Fold/Epoch: 0/98 [0/16193 (0%)]\ttrain_loss: 0.387805\n",
      "Train Fold/Epoch: 0/98 [1600/16193 (10%)]\ttrain_loss: 0.607522\n",
      "Train Fold/Epoch: 0/98 [3200/16193 (20%)]\ttrain_loss: 1.060043\n",
      "Train Fold/Epoch: 0/98 [4800/16193 (30%)]\ttrain_loss: 0.291784\n",
      "Train Fold/Epoch: 0/98 [6400/16193 (39%)]\ttrain_loss: 0.490949\n",
      "Train Fold/Epoch: 0/98 [8000/16193 (49%)]\ttrain_loss: 0.748863\n",
      "Train Fold/Epoch: 0/98 [9600/16193 (59%)]\ttrain_loss: 0.484417\n",
      "Train Fold/Epoch: 0/98 [11200/16193 (69%)]\ttrain_loss: 0.823877\n",
      "Train Fold/Epoch: 0/98 [12800/16193 (79%)]\ttrain_loss: 0.272347\n",
      "Train Fold/Epoch: 0/98 [14400/16193 (89%)]\ttrain_loss: 0.805215\n",
      "Train Fold/Epoch: 0/98 [16000/16193 (99%)]\ttrain_loss: 0.943593\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11865/16193 (73.27240%)\n",
      "Test set for fold 0:  Average test_loss:  0.8856, Accuracy: 2514/4049 (62.08940%)\n",
      "\n",
      "Train Fold/Epoch: 0/99 [0/16193 (0%)]\ttrain_loss: 0.367788\n",
      "Train Fold/Epoch: 0/99 [1600/16193 (10%)]\ttrain_loss: 0.586895\n",
      "Train Fold/Epoch: 0/99 [3200/16193 (20%)]\ttrain_loss: 0.992981\n",
      "Train Fold/Epoch: 0/99 [4800/16193 (30%)]\ttrain_loss: 0.294250\n",
      "Train Fold/Epoch: 0/99 [6400/16193 (39%)]\ttrain_loss: 0.553825\n",
      "Train Fold/Epoch: 0/99 [8000/16193 (49%)]\ttrain_loss: 0.947622\n",
      "Train Fold/Epoch: 0/99 [9600/16193 (59%)]\ttrain_loss: 0.586146\n",
      "Train Fold/Epoch: 0/99 [11200/16193 (69%)]\ttrain_loss: 0.806462\n",
      "Train Fold/Epoch: 0/99 [12800/16193 (79%)]\ttrain_loss: 0.272570\n",
      "Train Fold/Epoch: 0/99 [14400/16193 (89%)]\ttrain_loss: 0.798503\n",
      "Train Fold/Epoch: 0/99 [16000/16193 (99%)]\ttrain_loss: 0.907743\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11853/16193 (73.19830%)\n",
      "Test set for fold 0:  Average test_loss:  0.8816, Accuracy: 2529/4049 (62.45987%)\n",
      "\n",
      "Train Fold/Epoch: 0/100 [0/16193 (0%)]\ttrain_loss: 0.379032\n",
      "Train Fold/Epoch: 0/100 [1600/16193 (10%)]\ttrain_loss: 0.556575\n",
      "Train Fold/Epoch: 0/100 [3200/16193 (20%)]\ttrain_loss: 1.094767\n",
      "Train Fold/Epoch: 0/100 [4800/16193 (30%)]\ttrain_loss: 0.297937\n",
      "Train Fold/Epoch: 0/100 [6400/16193 (39%)]\ttrain_loss: 0.480812\n",
      "Train Fold/Epoch: 0/100 [8000/16193 (49%)]\ttrain_loss: 0.762904\n",
      "Train Fold/Epoch: 0/100 [9600/16193 (59%)]\ttrain_loss: 0.511550\n",
      "Train Fold/Epoch: 0/100 [11200/16193 (69%)]\ttrain_loss: 0.809249\n",
      "Train Fold/Epoch: 0/100 [12800/16193 (79%)]\ttrain_loss: 0.254929\n",
      "Train Fold/Epoch: 0/100 [14400/16193 (89%)]\ttrain_loss: 0.850066\n",
      "Train Fold/Epoch: 0/100 [16000/16193 (99%)]\ttrain_loss: 0.914545\n",
      "\n",
      "Train set for fold 0: Average train_loss: 0.0000, Accuracy: 11921/16193 (73.61823%)\n",
      "Test set for fold 0:  Average test_loss:  0.8861, Accuracy: 2533/4049 (62.55866%)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_NN1.to(device)\n",
    "\n",
    "# State fold (no PurgedKFold build yet, ignore this)\n",
    "# took about 1hour to train when epochs=300\n",
    "\n",
    "epochs=100\n",
    "fold = 0\n",
    "for epoch in range(1, epochs + 1):\n",
    "  correct_train, train_loss = train(fold, model_NN1, device, train_loader, optimizer, epoch)\n",
    "  test(fold, model_NN1, device, test_loader, correct_train, train_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 17
    },
    "id": "ndn_gLUxPWFc",
    "outputId": "1ee9ecf8-a098-4a45-a42f-4e5caaf36a02"
   },
   "outputs": [
    {
     "data": {
      "application/javascript": "\n    async function download(id, filename, size) {\n      if (!google.colab.kernel.accessAllowed) {\n        return;\n      }\n      const div = document.createElement('div');\n      const label = document.createElement('label');\n      label.textContent = `Downloading \"${filename}\": `;\n      div.appendChild(label);\n      const progress = document.createElement('progress');\n      progress.max = size;\n      div.appendChild(progress);\n      document.body.appendChild(div);\n\n      const buffers = [];\n      let downloaded = 0;\n\n      const channel = await google.colab.kernel.comms.open(id);\n      // Send a message to notify the kernel that we're ready.\n      channel.send({})\n\n      for await (const message of channel.messages) {\n        // Send a message to notify the kernel that we're ready.\n        channel.send({})\n        if (message.buffers) {\n          for (const buffer of message.buffers) {\n            buffers.push(buffer);\n            downloaded += buffer.byteLength;\n            progress.value = downloaded;\n          }\n        }\n      }\n      const blob = new Blob(buffers, {type: 'application/binary'});\n      const a = document.createElement('a');\n      a.href = window.URL.createObjectURL(blob);\n      a.download = filename;\n      div.appendChild(a);\n      a.click();\n      div.remove();\n    }\n  ",
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": "download(\"download_92e3dc14-f89f-4aba-bdb8-d1940fd4a8bd\", \"model_NN1.pkl\", 8466326)",
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Save model to disk and save in your own files to save you some time\n",
    "\n",
    "from google.colab import files\n",
    "\n",
    "filename = 'model_NN1'\n",
    "out = open(filename, 'wb')\n",
    "\n",
    "with open(filename + '.pkl', 'wb') as fid:\n",
    "  pickle.dump(model_NN1, fid)\n",
    "\n",
    "# load pickle file\n",
    "with open(filename + '.pkl', 'rb') as fid:\n",
    "     model_NN1 = pickle.load(fid)\n",
    "\n",
    "files.download(filename + '.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lTpeMX-zzTlU",
    "outputId": "4c068191-04d3-4c2d-f22c-b2a377a796db"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Net(\n",
       "  (fc1): Linear(in_features=12, out_features=1024, bias=True)\n",
       "  (relu1): ReLU()\n",
       "  (fc2): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "  (relu2): ReLU()\n",
       "  (fc3): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "  (hw1): Hardswish()\n",
       "  (fc_out): Linear(in_features=1024, out_features=3, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load pickle filetorch.from_numpy(y_test.astype(int)).long()\n",
    "\n",
    "filename = 'model_NN1'\n",
    "with open(filename + '.pkl', 'rb') as fid:\n",
    "     model_NN1_pickle = pickle.load(fid)\n",
    "model_NN1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "R8sdW9bVwPeh"
   },
   "source": [
    "## 3.4 Get classification report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "G1lBRHR97D4-",
    "outputId": "c4587227-d949-4b4f-96b0-b2ba33d24d67"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "labels in prediction: [0 1 2] \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "        long       0.60      0.65      0.62      1591\n",
      "      no bet       0.47      0.41      0.43       463\n",
      "       short       0.69      0.66      0.67      1995\n",
      "\n",
      "    accuracy                           0.63      4049\n",
      "   macro avg       0.58      0.57      0.58      4049\n",
      "weighted avg       0.63      0.63      0.62      4049\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "  # Show accuracy on test set\n",
    "  model_NN1.eval()\n",
    "\n",
    "  # predict proba\n",
    "  y_pred_nn1_proba = model_NN1(torch.from_numpy(X_test).float().to(device))\n",
    "  y_pred_nn1 = torch.argmax(y_pred_nn1_proba, dim=1)\n",
    "  y_pred_nn1 = y_pred_nn1.cpu().detach().numpy()\n",
    "\n",
    "# print predction values\n",
    "print('labels in prediction:', np.unique(y_pred_nn1), '\\n')\n",
    "\n",
    "# print report\n",
    "label_names = ['long', 'no bet', 'short']\n",
    "print(classification_report(y_test.astype(int), y_pred_nn1, target_names=label_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "3eT9tZiggKNQ",
    "outputId": "3c468314-963d-4a6d-c009-b0e8d12b22cd"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1723,  403, 1923])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.bincount(y_pred_nn1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "__1B0n8s4xhs"
   },
   "source": [
    "# Part 4: Feature Importance Analysis\n",
    "\n",
    "After we have a working neural network model (up to 66% accuracy with this network size), we can do a pertubation of the columns and do a prediction. When a column is pertubated and it delivers the highest error, that means that column in most important for the prediction of the action."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qxxH6ZasFthF"
   },
   "source": [
    "## Pertubation Rank (PR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "as0aJEnv1gSP"
   },
   "outputs": [],
   "source": [
    "def perturbation_rank(model,x,y,names):\n",
    "    errors = []\n",
    "\n",
    "    X_saved = x\n",
    "    y = y.flatten()\n",
    "\n",
    "    with torch.no_grad():\n",
    "        model.eval()\n",
    "        for i in range(x.shape[1]):\n",
    "\n",
    "            # Convert to numpy, shuffle, convert back to tensor, predict\n",
    "            x = x.detach().numpy()\n",
    "            np.random.shuffle(x[:,i])\n",
    "            x = torch.from_numpy(x).float().to(device)\n",
    "            pred = model(x)\n",
    "\n",
    "            # log_loss requires (classification target, probabilities)\n",
    "            pred = pred.cpu().detach().numpy()\n",
    "            error = metrics.log_loss(y, pred)\n",
    "            errors.append(error)\n",
    "\n",
    "            # Reset x to saved tensor matrix\n",
    "            x = X_saved\n",
    "    \n",
    "    max_error = np.max(errors)\n",
    "    importance = [e/max_error for e in errors]\n",
    "    \n",
    "    data = {'name':names,'error':errors,'importance':importance}\n",
    "    result = pd.DataFrame(data,columns = ['name','error','importance'])\n",
    "    result.sort_values(by=['importance'],ascending=[0],inplace=True)\n",
    "    result.reset_index(inplace=True,drop=True)\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DbGA923ZiPzv",
    "outputId": "9579a0c0-b882-48eb-be30-f62850d1c50b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Net(\n",
       "  (fc1): Linear(in_features=12, out_features=1024, bias=True)\n",
       "  (relu1): ReLU()\n",
       "  (fc2): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "  (relu2): ReLU()\n",
       "  (fc3): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "  (hw1): Hardswish()\n",
       "  (fc_out): Linear(in_features=1024, out_features=3, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_NN1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 426
    },
    "id": "tclwZN-fjXED",
    "outputId": "85bd7b60-b3ab-481a-de39-2587745d940f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-7f34aae1-f9ad-407d-8153-b846574d2779\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>error</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>volatility</td>\n",
       "      <td>15.432391</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cci</td>\n",
       "      <td>15.258762</td>\n",
       "      <td>0.988749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>macd_hist</td>\n",
       "      <td>15.253956</td>\n",
       "      <td>0.988438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dx</td>\n",
       "      <td>15.084260</td>\n",
       "      <td>0.977442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>macd_signal</td>\n",
       "      <td>15.032029</td>\n",
       "      <td>0.974057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>macd</td>\n",
       "      <td>14.865012</td>\n",
       "      <td>0.963235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>rsi</td>\n",
       "      <td>14.755884</td>\n",
       "      <td>0.956163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>close</td>\n",
       "      <td>13.985946</td>\n",
       "      <td>0.906272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>volume</td>\n",
       "      <td>13.955781</td>\n",
       "      <td>0.904317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>low</td>\n",
       "      <td>13.012315</td>\n",
       "      <td>0.843182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>high</td>\n",
       "      <td>12.095231</td>\n",
       "      <td>0.783756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>open</td>\n",
       "      <td>10.876494</td>\n",
       "      <td>0.704783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7f34aae1-f9ad-407d-8153-b846574d2779')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-7f34aae1-f9ad-407d-8153-b846574d2779 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-7f34aae1-f9ad-407d-8153-b846574d2779');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "           name      error  importance\n",
       "0    volatility  15.432391    1.000000\n",
       "1           cci  15.258762    0.988749\n",
       "2     macd_hist  15.253956    0.988438\n",
       "3            dx  15.084260    0.977442\n",
       "4   macd_signal  15.032029    0.974057\n",
       "5          macd  14.865012    0.963235\n",
       "6           rsi  14.755884    0.956163\n",
       "7         close  13.985946    0.906272\n",
       "8        volume  13.955781    0.904317\n",
       "9           low  13.012315    0.843182\n",
       "10         high  12.095231    0.783756\n",
       "11         open  10.876494    0.704783"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names = list(data_ohlcv.columns)\n",
    "names.remove('label_barrier')\n",
    "rank = perturbation_rank(model_NN1, \n",
    "                         torch.from_numpy(X_test).float(),\n",
    "                         torch.from_numpy(y_test.astype(int)).long(),  \n",
    "                         names\n",
    "                         )\n",
    "\n",
    "display(rank)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 426
    },
    "id": "dynI8Dd6hiv1",
    "outputId": "acffb113-31a6-4f73-b6f6-7f7bda8530f3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-91ed0c32-731a-4fd8-a622-0375d5d8b704\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>error</th>\n",
       "      <th>importance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>dx</td>\n",
       "      <td>18.006353</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>cci</td>\n",
       "      <td>17.788374</td>\n",
       "      <td>0.987894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>volatility</td>\n",
       "      <td>17.731627</td>\n",
       "      <td>0.984743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>macd_hist</td>\n",
       "      <td>17.570308</td>\n",
       "      <td>0.975784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>macd_signal</td>\n",
       "      <td>17.191725</td>\n",
       "      <td>0.954759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>macd</td>\n",
       "      <td>17.056508</td>\n",
       "      <td>0.947249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>volume</td>\n",
       "      <td>17.019973</td>\n",
       "      <td>0.945220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>rsi</td>\n",
       "      <td>16.921982</td>\n",
       "      <td>0.939778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>close</td>\n",
       "      <td>16.450972</td>\n",
       "      <td>0.913620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>low</td>\n",
       "      <td>15.693735</td>\n",
       "      <td>0.871567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>high</td>\n",
       "      <td>14.698744</td>\n",
       "      <td>0.816309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>open</td>\n",
       "      <td>12.228066</td>\n",
       "      <td>0.679097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-91ed0c32-731a-4fd8-a622-0375d5d8b704')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-91ed0c32-731a-4fd8-a622-0375d5d8b704 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-91ed0c32-731a-4fd8-a622-0375d5d8b704');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "           name      error  importance\n",
       "0            dx  18.006353    1.000000\n",
       "1           cci  17.788374    0.987894\n",
       "2    volatility  17.731627    0.984743\n",
       "3     macd_hist  17.570308    0.975784\n",
       "4   macd_signal  17.191725    0.954759\n",
       "5          macd  17.056508    0.947249\n",
       "6        volume  17.019973    0.945220\n",
       "7           rsi  16.921982    0.939778\n",
       "8         close  16.450972    0.913620\n",
       "9           low  15.693735    0.871567\n",
       "10         high  14.698744    0.816309\n",
       "11         open  12.228066    0.679097"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "names = list(data_ohlcv.columns)\n",
    "names.remove('label_barrier')\n",
    "rank = perturbation_rank(model_NN1, \n",
    "                         torch.from_numpy(X_test).float(),\n",
    "                         torch.from_numpy(y_test.astype(int)).long(),  \n",
    "                         names\n",
    "                         )\n",
    "\n",
    "display(rank)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "VJOhQjyajKf_"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "Crypto_Feature_Importance.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "name": "python3"
  },
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
